Challenges, Development and Frontiers of Smart Agriculture and Forestry

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Topic Information

Dear Colleagues,

With the development of agriculture and forestry stepping into the digital era, the digital technology of agricultural and forestry is becoming increasingly used. We call it smart agriculture and forestry, which is characterized by “information + knowledge + intelligent equipment”, realizing the deep cross-domain integration of modern information technology and agriculture and forestry. In the future, the development of smart agriculture and forestry will focus on information and intelligent technology in order to build an integrated platform for smart perception, wireless transmission, smart decision making and intelligent control so as to realize the intelligent supervision of the entire agricultural and forestry industry chain (production, processing, operation, management and service) and finally achieve the purpose of cost reduction, quality improvement and efficiency increase. The rapid development of artificial intelligence, UAV, remote sensing, Big Data analysis and other technologies provides substantial support for agriculture and forestry to move forward into the intelligent era. This topic aims to fully tap frontier technologies including Internet of Things, multi-modal and multi-angle stereo observation, cloud computing, Big Data, machine learning, intelligent decision-making, etc., thereby exploring its application potential in smart agriculture and forestry and accelerating the development of precision agriculture and forestry.

The topic “Challenges, Development and Frontiers of Smart Agriculture and Forestry” welcomes high-quality studies that focus on the innovation of five core technologies including remote sensing, advanced sensor, Big Data and cloud services, precision operation technology and equipment, Internet of Things and Robots and the solutions, strategies, pilot cases and examples of these frontier technologies applied in the fields of forest and farmland investigation and change monitoring; plant protection; disaster prevention; precision cultivation; visual management; ecological protection and reconstruction; and evaluation of ecological service value. Relevant themes include but are not limited to the following.

Intelligent sensing technology and equipment for agriculture and Forestry

• Advanced agroforestry sensors and data analysis
• Intelligent robot integrating cloud computing, Big Data and machine learning technology
• Intelligent agricultural and forestry machinery equipment and technology
• Development and test of agricultural and forestry intelligent equipment

New methods and technologies for precision forestry investigation, monitoring and evaluation

• New methods and technology of forest resources investigation based on multi-source remote sensing
• Forest change and disturbance detection using multi-modal remote sensing technology
• Forest degradation–restoration monitoring combining remote sensing and geographic Big Data
• Forest disaster monitoring and prediction integrating remote sensing and geographic Big Data
• Precision estimation and modelling of forest structure and function parameters
• Forest visualization and management

Technological innovation in agriculture 4.0 era

• Innovative sensors and technologies for smart agriculture
• Application of deep learning and geographic Big Data in smart breeding and germplasm resources
• Smart technologies and decision support for precision orchard production
• Multi-technology integration for high-throughput plant phenotyping
• Intelligent in situ monitoring and decision-making of crop typical stress

Sustainable development of agriculture and forestry ecology

• Assessment of ecosystem service function based on geographic Big Data analysis
• Precise estimation of agricultural and forestry carbon storage
• Vegetation species diversity evaluation based on remote sensing and machine learning
• Agriculture and forestry approaches and contributions to achieve carbon neutralization
• Challenges and opportunities to the construction of smart agriculture and forestry

Prof. Dr. Xiaoli Zhang
Prof. Dr. Dengsheng Lu
Prof. Dr. Xiujuan Chai
Prof. Dr. Guijun Yang
Dr. Langning Huo
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	3.6	2011	17.7 Days	CHF 2600
Agronomy agronomy	3.7	5.2	2011	15.8 Days	CHF 2600
Forests forests	2.9	4.5	2010	16.9 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

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Published Papers (37 papers)

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18 pages, 8981 KiB  

Open AccessArticle

Distribution of Grazing Paths and Their Influence on Mountain Vegetation in the Traditional Grazing Area of the Tien-Shan Mountains

by Xiang Jia, Tiecheng Huang, Mengyu Chen, Ning Han, Yihao Liu, Shujiang Chen and Xiaoli Zhang

Remote Sens. 2023, 15(12), 3163; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123163 - 17 Jun 2023

Viewed by 1055

Abstract

In the Tien-Shan Mountains, Ili Prefecture, Xinjiang, China, the livestock industry has experienced rapid growth in recent decades. However, this expansion has led to increased overgrazing behavior, resulting in the proliferation of grazing paths and a decline in vegetation cover. These factors are [...] Read more.

In the Tien-Shan Mountains, Ili Prefecture, Xinjiang, China, the livestock industry has experienced rapid growth in recent decades. However, this expansion has led to increased overgrazing behavior, resulting in the proliferation of grazing paths and a decline in vegetation cover. These factors are considered the main causes of vegetation degradation in the region. To investigate this issue, we conducted a study utilizing unmanned aerial vehicle imagery in the Zollersay Mountains of Ili to examine the distribution of grazing paths and their effects on mountain vegetation, including grassland and Malus sieversii. The results of our study revealed that grazing paths in the area exhibited various formations, including parallel, oblique intersection, and grid. On the hilltop, the grazing paths were not only shorter but also wider, whereas on the hillside, they were denser, indicating a higher concentration of livestock trampling events. It was found that grazing path density played a pivotal role in grassland degradation, with a negative correlation observed between grazing path density and indicators such as the grassland quality index and grass vegetation coverage. As grazing path density increased, the damage inflicted on Malus sieversii by livestock also intensified. However, as the trees grow older, their height surpasses the feeding range of livestock, resulting in reduced grazing impact. The findings of our study carry significant implications for developing scientifically informed livestock policies and promoting the conservation of wild fruit forests. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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25 pages, 7777 KiB  

Open AccessArticle

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

by Jun Li, Sifan Wang, Wenyu Zhang, Haomin Li, Ye Zeng, Tao Wang, Ke Fei, Xinrui Qiu, Runpeng Jiang, Chaodong Mai and Yachao Cao

Agronomy 2023, 13(5), 1395; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13051395 - 18 May 2023

Viewed by 1319

Abstract

In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This [...] Read more.

In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This not only affects the accuracy of path tracking but also leads to unstable motion for the mowing robots. To solve the above problems, we take an orchard mowing robot as the control object and establish a cascaded path-tracking controller and an adaptive time domain model based on a kinematics model. By designing a linear error model, an objective function, and constraint conditions for the mowing robot, the optimal linear velocity and angular velocity of the mower are obtained and converted into the speed of the driving wheel. Then, an anti-slip driving controller is designed based on fuzzy control of the slip rate. The slip-rate-based fuzzy controller is constructed according to the real-time speed of the mower and the reference speed of the driving wheel solved by the model predictive controller, and anti-slip driving control is implemented through a combination of a PID controller and a tire dynamics model. To verify the effectiveness of the proposed method, simulation and field experiments are conducted. The experimental results show that the slip rate of the driving wheel of the mower remains within the target slip rate range in the orchard working environment, avoiding excessive driving wheel sliding. Furthermore, the average lateral error of the path-tracking controller is controlled within 0.05 m, and the average value of the longitudinal error is kept within 0.04 m, which satisfies the control accuracy requirements of lawn mower operations. The proposed method provides a reference optimization scheme for improving the path-tracking and motion stability of a mowing robot. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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22 pages, 12268 KiB  

Open AccessArticle

Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

by Hanhu Liu, Xiangqi Lei, Hui Liang and Xiao Wang

Sustainability 2023, 15(9), 7038; https://0-doi-org.brum.beds.ac.uk/10.3390/su15097038 - 22 Apr 2023

Cited by 1 | Viewed by 1191

Abstract

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of [...] Read more.

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R² = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R² = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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19 pages, 7278 KiB  

Open AccessArticle

Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra

by Jianxin Yin, Zhan Shi, Baoguo Li, Fujun Sun, Tianyu Miao, Zhou Shi, Songchao Chen, Meihua Yang and Wenjun Ji

Remote Sens. 2023, 15(8), 2053; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082053 - 13 Apr 2023

Cited by 2 | Viewed by 2045

Abstract

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research [...] Read more.

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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15 pages, 4430 KiB  

Open AccessArticle

Estimates of Dust Emissions and Organic Carbon Losses Induced by Wind Erosion in Farmland Worldwide from 2017 to 2021

by Yongxiang Liu, Hongmei Zhao, Guangying Zhao, Xinyuan Cao, Xuelei Zhang and Aijun Xiu

Agriculture 2023, 13(4), 781; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13040781 - 28 Mar 2023

Cited by 1 | Viewed by 1502

Abstract

Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive [...] Read more.

Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive to develop carbon-neutral practices. However, the amount of global carbon lost due to the wind erosion of farmland is unknown. In this study, global farmland dust emissions were estimated from a dust emission inventory (0.1° × 0.1°, daily) built using the improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), and global farmland organic carbon losses were estimated by combining this with global soil organic carbon concentration data. The average global annual dust emissions from agricultural land from 2017 to 2021 were 1.75 × 10⁹ g/s. Global dust emissions from agricultural land are concentrated in the UK, Ukraine, and Russia in Europe; in southern Canada and the central US in North America; in the area around Buenos Aires, the capital of Argentina, in South America; and in northeast China in Asia. The global average annual organic carbon loss from agricultural land was 2970 Gg for 2017–2021. The spatial distribution of emissions is roughly consistent with that of dust emissions, which are mainly concentrated in the world’s four major black soil regions. These estimates of dust and organic carbon losses from agricultural land are essential references that can inform the global responses to the carbon cycle, dust emissions, and black soil conservation. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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10 pages, 1445 KiB  

Open AccessArticle

Technology Transfer Model for Small-Scale Farms

by Leidy Y. Flórez Gómez, Johanna Pico Mendoza, Cesar D. Guerrero and Alexandra Espinosa Carreño

Sustainability 2023, 15(6), 5320; https://0-doi-org.brum.beds.ac.uk/10.3390/su15065320 - 17 Mar 2023

Cited by 1 | Viewed by 2230

Abstract

Small-scale farms make an important contribution to food security, but they lack technification, especially in the global south. This article proposes a new model, namely, Model H, as a reference by which to facilitate technological transfer and appropriation in small producer sectors. Starting [...] Read more.

Small-scale farms make an important contribution to food security, but they lack technification, especially in the global south. This article proposes a new model, namely, Model H, as a reference by which to facilitate technological transfer and appropriation in small producer sectors. Starting with the identification of interactions with the environment and the characterization of the transfer and appropriation process with respect to information and communication technologies, a five-stage framework is established to create and validate the new model. Based on key elements, functionalities, and five variables identified as a common ground for the transfer and appropriation of technologies, Model H is presented as a five-layer, user-centered model that aims to include in the transfer and appropriation of the solutions of all the individuals and entities that participate throughout the process. The model is validated through a pilot test using an intelligent irrigation technology called AgroRIEGO. In the process of technology transfer and appropriation, this pilot study helped to identify implementation obstacles and the importance of knowledge management as an effective channel for the exchange of information in a pertinent and timely manner. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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20 pages, 4851 KiB  

Open AccessEditor’s ChoiceArticle

Design of an Intermittent Herbicide Spray System for Open-Field Cabbage and Plant Protection Effect Experiments

by Shenyu Zheng, Xueguan Zhao, Xinwei Zhang, Hao Fu, Kechuan Yi and Changyuan Zhai

Agronomy 2023, 13(2), 286; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13020286 - 17 Jan 2023

Cited by 5 | Viewed by 1707

Abstract

To address the problem of herbicide residues exceeding the safety standard due to continuous spraying of herbicides on open-field cabbage, we propose an intermittent weed spraying control method integrating cabbage position, cabbage canopy size, and spraying machine operation speed. It is based on [...] Read more.

To address the problem of herbicide residues exceeding the safety standard due to continuous spraying of herbicides on open-field cabbage, we propose an intermittent weed spraying control method integrating cabbage position, cabbage canopy size, and spraying machine operation speed. It is based on an early-stage cabbage target identification method obtained in the early stage and the operation requirements in open-field cabbage. Built with a C37 controller, a stable pressure spray system and an intermittent weed spraying control system for open-field cabbage, an integrated system was designed. Experimental verification was carried out through measurement indexes such as spraying precision, herbicide saving rate, herbicide efficacy, and herbicide residue. Since the industry is faced with a status quo of a lack of relevant operational norms and national standards for the precise weed spraying operation mode, this paper provides a relatively perfect experiment and evaluation method for this mode. The experimental results on the accuracy of weed spraying at different speeds showed that the mean absolute error (MAE), root mean square error (RMSE), and average spray cabbage coverage rate (ASCCR) of intermittent weed spraying increased, but the average effective spray coverage rate (AESCR) decreased with increasing operation speed. When the working speed was 0.51 m/s, the MAE and RMSE of intermittent weed spraying were less than 2.87 cm and 3.40 cm, respectively, and the AESCR was 98.4%, which verified the feasibility of operating the intermittent weed spraying of cabbage. The results of a field experiment showed that the average weed-killing rate of intermittent weed spraying for open-field cabbage was 94.8%, and the herbicide-saving rate could reach 28.3% for a similar weeding effect to that of constant-rate application, which not only met the needs of intermittent weed spraying in open-field cabbage but also had great significance for improving the herbicide utilization rate. Compared with the constant-rate application method, the herbicide residue concentration detected using intermittent weed spraying for cabbage decreased by 66.6% on average, which has important research significance and application value for ensuring the normal growth of crops and the safety of agricultural products. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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20 pages, 3145 KiB  

Open AccessArticle

Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm

by Cong Chen, Yibai Li, Guangqiao Cao and Jinlong Zhang

Sustainability 2023, 15(3), 1772; https://0-doi-org.brum.beds.ac.uk/10.3390/su15031772 - 17 Jan 2023

Cited by 7 | Viewed by 1401

Abstract

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of [...] Read more.

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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19 pages, 5038 KiB  

Open AccessArticle

Remote Sensing Monitoring of Pine Wilt Disease Based on Time-Series Remote Sensing Index

by Lin Long, Yuanyuan Chen, Shaojun Song, Xiaoli Zhang, Xiang Jia, Yagang Lu and Gao Liu

Remote Sens. 2023, 15(2), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020360 - 06 Jan 2023

Cited by 6 | Viewed by 4199

Abstract

Under the strong influence of climate change and human activities, the frequency and intensity of disturbance events in the forest ecosystem both show significant increasing trends. Pine wood nematode (Bursapherenchus xylophilus, PWN) is one of the major alien invasive species in [...] Read more.

Under the strong influence of climate change and human activities, the frequency and intensity of disturbance events in the forest ecosystem both show significant increasing trends. Pine wood nematode (Bursapherenchus xylophilus, PWN) is one of the major alien invasive species in China, which has rapidly infected the forest and spread. In recent years, its tendency has been to spread from south to north, causing serious losses to Pinus and non-Pinus coniferous forests. It is urgent to carry out remote sensing monitoring and prediction of pine wilt disease (PWD). Taking Anhui Province as the study area, we applied ground survey, satellite-borne optical remote sensing imagery and environmental factor statistics, relying on the Google Earth Engine (GEE) platform to build a new vegetation index NDFI based on time-series Landsat images to extract coniferous forest information and used a random forest classification algorithm to build a monitoring model of the PWD infection stage. The results show that the proposed NDFI differentiation threshold classification method can accurately extract the coniferous forest range, with the overall accuracy of 87.75%. The overall accuracy of the PWD monitoring model based on random forest classification reaches 81.67%, and the kappa coefficient is 0.622. High temperature and low humidity are conducive to the survival of PWN, which aggravates the occurrence of PWD. Under the background of global warming, the degree of PWD in Anhui Province has gradually increased, and has transferred from the southwest and south to the middle and northeast. Our results show that PWD monitoring and prediction at a regional scale can be realized by using long time-series multi-source remote sensing data, NDFI index can accurately extract coniferous forest information and grasp disease information in a timely manner, which is crucial for effective monitoring and control of PWD. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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15 pages, 2102 KiB  

Open AccessArticle

Spatial–Temporal Evolution and Driving Factors of Habitat Quality in Malus sieversii Forest Areas in the Western Tianshan Mountain’s Watersheds

by Mengyu Chen, Hejuan Fan, Xiaoli Zhang, Fengbin Lai, Xiang Jia, Tiecheng Huang and Yihao Liu

Forests 2023, 14(1), 104; https://0-doi-org.brum.beds.ac.uk/10.3390/f14010104 - 05 Jan 2023

Cited by 1 | Viewed by 1250

Abstract

The landscape pattern of Xinjiang’s wild apple forest (Malus sieversii) area has undergone substantial change due to human activity disruption and frequent natural catastrophes. This change has a significant influence on the biodiversity and stability of the ecosystem. This study aimed [...] Read more.

The landscape pattern of Xinjiang’s wild apple forest (Malus sieversii) area has undergone substantial change due to human activity disruption and frequent natural catastrophes. This change has a significant influence on the biodiversity and stability of the ecosystem. This study aimed to evaluate the spatial and temporal evolution in habitat quality and landscape pattern changes to analyze the underlying factors affecting habitat quality in the Malus sieversii forest (MF) area in the Mohe watershed of the western Tianshan Mountains. Here, we applied the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, using four periods of remote sensing images of 1964, 1980, 2000, and 2017 as data sources, and analyzed the trend of landscape pattern changes in the MF area. The results show that (1) from 1964 to 2017, the study area was greatly affected by anthropogenic disturbance and climate change. Each landscape index indicates that the fragmentation of the whole study area has increased, the stability of the ecosystem has weakened, and the habitat quality is somewhat in jeopardy. (2) Analyzed in terms of spatial and temporal characteristics, the habitat quality of the whole study area decreased from 1964 to 2017. Among them, the low habitat value is mainly distributed in the north and northeast, the central part of the study area shows scattered low-habitat-value areas, and in the high-altitude area in the south, the ecosystem is more stable. (3) Since the northern region is dominated by cultivated land patches and residential building land patches, the habitat quality of the stressed zone deteriorates the larger its maximum patch area. The habitat quality of the region under stress worsens the larger its maximum patch size. In the area dominated by MF, the larger the area of MF patches, the higher the ecological service value. The study may be helpful for comprehending how the dynamics of landscape patterns affect biodiversity. It may also offer a scientific foundation for improving regional natural environments and effective decision-making support for local governments in the areas of landscape design and biodiversity preservation. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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22 pages, 621 KiB  

Open AccessArticle

From Farmers’ Entrepreneurial Motivation to Performance—The Chain Mediating Effect of Entrepreneurial Learning and Entrepreneurial Ability

by Shiyuan Yang, Mengjia Li, Longhua Yue, Lina Yu and Wei Li

Sustainability 2023, 15(1), 726; https://0-doi-org.brum.beds.ac.uk/10.3390/su15010726 - 31 Dec 2022

Viewed by 1778

Abstract

Farmers’ entrepreneurship is an important measure to achieve the stable development of rural areas. However, the performance of farmers’ entrepreneurship is generally low. How to improve the performance to promote farmers’ sustainable entrepreneurship has become the primary problem. Therefore, based on the entrepreneurial [...] Read more.

Farmers’ entrepreneurship is an important measure to achieve the stable development of rural areas. However, the performance of farmers’ entrepreneurship is generally low. How to improve the performance to promote farmers’ sustainable entrepreneurship has become the primary problem. Therefore, based on the entrepreneurial process theory, this paper takes entrepreneurial farmers who participated in the cultivation of new vocational farmers in Sichuan Province from 2018 to 2021 as the research object, collects 329 valid sample data through questionnaires, and empirically tests the impact of farmers’ dual entrepreneurial motivation on entrepreneurial performance, as well as the chain intermediary role of entrepreneurial learning and entrepreneurial ability. The results show that: survival entrepreneurial motivation and opportunity entrepreneurial motivation both have significant positive impacts on entrepreneurial learning, entrepreneurial ability, and entrepreneurial performance; entrepreneurial learning plays a complete intermediary role between dual entrepreneurial motivation and entrepreneurial performance, entrepreneurial ability plays a complete intermediary role between dual entrepreneurial motivation and entrepreneurial performance, and entrepreneurial learning and entrepreneurial ability play a complete chain intermediary role between dual entrepreneurial motivation and entrepreneurial performance. The research expands a new perspective on the path and mechanism of entrepreneurial motivation on entrepreneurial performance, and proposes measures to stimulate farmers’ entrepreneurial motivation, improve the entrepreneurial training system, and build a learning and exchange platform, which are of great practical significance to improve farmers’ entrepreneurial performance. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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21 pages, 9220 KiB  

Open AccessArticle

A Comparison of Four Methods for Automatic Delineation of Tree Stands from Grids of LiDAR Metrics

by Yusen Sun, Xingji Jin, Timo Pukkala and Fengri Li

Remote Sens. 2022, 14(24), 6192; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246192 - 07 Dec 2022

Viewed by 1300

Abstract

Increased use of laser scanning in forest inventories is leading to the adoption and development of automated stand delineation methods. The most common categories of these methods are region merging and region growing. However, recent literature proposes alternative methods that are based on [...] Read more.

Increased use of laser scanning in forest inventories is leading to the adoption and development of automated stand delineation methods. The most common categories of these methods are region merging and region growing. However, recent literature proposes alternative methods that are based on the ideas of cellular automata, self-organizing maps, and combinatorial optimization. The studies where these methods have been described suggest that the new methods are potential options for the automated segmentation of a forest into homogeneous stands. However, no studies are available that compare the new methods to each other and to the traditional region-merging and region-growing algorithms. This study provided a detailed comparison of four methods using LiDAR metrics calculated for grids of 5 m by 5 m raster cells as the data. The tested segmentation methods were region growing (RG), cellular automaton (CA), self-organizing map (SOM), and simulated annealing (SA), which is a heuristic algorithm developed for combinatorial optimization. The case study area was located in the Heilongjiang province of northeast China. The LiDAR data were collected from an unmanned aerial vehicle for three 1500-ha test areas. The proportion of variation in the LiDAR metrics that was explained by the segmentation was mostly the best for the SA method. The RG method produced more heterogeneous segments than the other methods. The CA method resulted in the smallest number of segments and the largest average segment area. The proportion of small segments (smaller than 0.3 ha) was the highest in the RG method while the SA method always produced the fewest small stands. The shapes of the segments were the best (most circular) for the CA and SA methods, but the shape metrics were good for all methods. The results of the study suggest that CA, SOM, and SA may all outperform RG in automated stand delineation. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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22 pages, 5116 KiB  

Open AccessArticle

Stability Study of Time Lag Disturbance in an Automatic Tractor Steering System Based on Sliding Mode Predictive Control

by Hequan Miao, Peisong Diao, Wenyan Yao, Shaochuan Li and Wenjun Wang

Agriculture 2022, 12(12), 2091; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12122091 - 06 Dec 2022

Cited by 2 | Viewed by 1279

Abstract

To improve the working accuracy and anti-interference capability of the steering operation of an automatic tractor, this paper investigates the tractor steering system. In response to the current problems of high steering resistance during tractor field operations and the low service life of [...] Read more.

To improve the working accuracy and anti-interference capability of the steering operation of an automatic tractor, this paper investigates the tractor steering system. In response to the current problems of high steering resistance during tractor field operations and the low service life of the drive shaft of conventional electric steering wheel solutions, an electro-hydraulic coupled power-assisted solution combining EPS and HPS is proposed. The combination of the EPS system’s high control accuracy and sensitive steering operation and the hydraulic power system’s large steering torque greatly reduces the power of the power motor and battery performance requirements, optimizing the power transmission scheme to achieve green and energy-saving purposes. Secondly, the research is focused on the influence of external disturbances on the stability of the steering system during tractor operation, and a combination of model predictive control and sliding mode control is used to study the steering system control strategy. It is finally demonstrated through simulations and experiments that it can compensate for the disturbance of the system control parameters by external disturbances, has the ability of MPC to handle input constraints and maintains the advantages of SMC robustness. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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18 pages, 3988 KiB  

Open AccessFeature PaperArticle

Spatial Distribution Pattern of Root Sprouts under the Canopy of Malus sieversii in a Typical River Valley on the Northern Slopes of the Tianshan Mountain

by Li Li, Mengyu Chen, Xiaoli Zhang and Xiang Jia

Forests 2022, 13(12), 2044; https://0-doi-org.brum.beds.ac.uk/10.3390/f13122044 - 01 Dec 2022

Cited by 2 | Viewed by 1302

Abstract

Malus sieversii is a precious wild fruit tree resource, and its sustainable reproduction is of great significance to the conservation of wild fruit tree germplasm resources and the stability of wild fruit forest ecosystems. In recent years, the natural population number and area [...] Read more.

Malus sieversii is a precious wild fruit tree resource, and its sustainable reproduction is of great significance to the conservation of wild fruit tree germplasm resources and the stability of wild fruit forest ecosystems. In recent years, the natural population number and area of distribution of the Malus sieversii have been declining due to pests, water limitations, and human activities. Root sprouts are a primary means of rejuvenation of the Malus sieversii. A reasonable spatial distribution pattern is conducive to the growth of Malus sieversii plants and the ecological restoration of wild fruit forest populations. However, the spatial distribution pattern of root sprouts still needs to be discovered, which constrains our understanding of the mechanisms underlying the damage and management of Malus sieversii. Therefore, this paper examines the study area of the Gilgalang River Malus sieversii forest in Gongliu County, Ili Valley, Xinjiang. The topographic data and high-resolution images were first obtained using ultra-low-altitude photogrammetry and total station measurement techniques, then spatial pattern analysis and standard deviation ellipse analysis were used to investigate the spatial distribution pattern of root sprouts, and, finally, the factors affecting the spatial distribution pattern of root sprouts were investigated by principal component analysis and grey correlation analysis. The results show that: (1) Under-canopy Malus sieversii root sprouts are clustered and randomly distributed along the root system, with the degree of clustering decreasing with increasing distance; (2) Spatial orientation and distance from the maternal plant are the main factor affecting the sprouting of Malus sieversii roots, explaining 73.69% of the total variance; (3) Under sediment accumulation and water erosion, the root sprouts under the canopy are mainly distributed in the downslope direction. The shape is similar to the “clover type”. The results of this study can provide a theoretical basis for conserving Malus sieversii germplasm resources and a solid scientific basis for the ecological restoration of plants under anthropogenic disturbance. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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23 pages, 9164 KiB  

Open AccessArticle

Detection of Parthenium Weed (Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence

by Benjamin Costello, Olusegun O. Osunkoya, Juan Sandino, William Marinic, Peter Trotter, Boyang Shi, Felipe Gonzalez and Kunjithapatham Dhileepan

Agriculture 2022, 12(11), 1838; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12111838 - 02 Nov 2022

Cited by 7 | Viewed by 2743

Abstract

Parthenium weed (Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has [...] Read more.

Parthenium weed (Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has infested both agricultural and conservation lands, including riparian corridors. Effective control strategies for this weed (pasture management, biological control, and herbicide usage) require populations to be detected and mapped. However, the mapping is made difficult due to varying nature of the infested landscapes (e.g., uneven terrain). This paper proposes a novel method to detect and map parthenium populations in simulated pastoral environments using Red-Green-Blue (RGB) and/or hyperspectral imagery aided by artificial intelligence. Two datasets were collected in a control environment using a series of parthenium and naturally co-occurring, non-parthenium (monocot) plants. RGB images were processed with a YOLOv4 Convolutional Neural Network (CNN) implementation, achieving an overall accuracy of 95% for detection, and 86% for classification of flowering and non-flowering stages of the weed. An XGBoost classifier was used for the pixel classification of the hyperspectral dataset—achieving a classification accuracy of 99% for each parthenium weed growth stage class; all materials received a discernible colour mask. When parthenium and non-parthenium plants were artificially combined in various permutations, the pixel classification accuracy was 99% for each parthenium and non-parthenium class, again with all materials receiving an accurate and discernible colour mask. Performance metrics indicate that our proposed processing pipeline can be used in the preliminary design of parthenium weed detection strategies, and can be extended for automated processing of collected RGB and hyperspectral airborne unmanned aerial vehicle (UAV) data. The findings also demonstrate the potential for images collected in a controlled, glasshouse environment to be used in the preliminary design of invasive weed detection strategies in the field. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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8 pages, 221 KiB  

Open AccessArticle

The Effect of Different Environmental Factors on Milk Yield Characteristics of Holstein Fresian Cattle Raised with Different Production Scale on Teke Region of Turkey

by Cevat Sipahi

Sustainability 2022, 14(21), 13802; https://0-doi-org.brum.beds.ac.uk/10.3390/su142113802 - 25 Oct 2022

Cited by 1 | Viewed by 1144

Abstract

This study was intended to determine whether there was any difference between the parameters of herd size and milk yield based on the hypothesis that the dairy cattle enterprises in the Teke Region used different production methods depending on their herd size. Total [...] Read more.

This study was intended to determine whether there was any difference between the parameters of herd size and milk yield based on the hypothesis that the dairy cattle enterprises in the Teke Region used different production methods depending on their herd size. Total milk yield and 305-d milk yield were increased in parallel with the farm-scale and reached 8968.70 ± 124.56 kg and 7632.20 ± 79.67 kg, respectively, in the farms with the largest scale of 101 heads and above (p < 0.001). It was further determined that milk yield decreased significantly in the summer calving season compared to other seasons (Summer: 7897.20 ± 154.48 ^b, Autumn: 8344.80 ± 169.33 ^a, Winter: 8054.50 ± 127.22 ^a, Spring: 8133.60 ± 159.77 ^a) (p < 0.01). Heat stress is thought to be the reason for the low milk yield in the summer season compared to other seasons. It was shown that the small-scale farms with 1–10 cows had the longest lactation length (394.90 ± 6.90 days) (p < 0.001). It was also determined that there is a directly proportional and significant relationship between the lactation number of Holstein cattle and lactation milk yield and 305-d milk yield values (p < 0.01). It was determined that dairy cattle in the 5th lactation had the highest 305-d milk yield value with 6992.00 ± 164.40 kg. In conclusion, a positive statistical correlation was found between the scale of dairy farms and their milk production parameter. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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Supplementary material:
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23 pages, 5745 KiB  

Open AccessArticle

Occurrence Prediction of Pine Wilt Disease Based on CA–Markov Model

by Deqing Liu and Xiaoli Zhang

Forests 2022, 13(10), 1736; https://0-doi-org.brum.beds.ac.uk/10.3390/f13101736 - 20 Oct 2022

Cited by 1 | Viewed by 1485

Abstract

Pine wilt disease (PWD) has become a devastating disease that impacts China’s forest management. It is of great significance to accurately predict PWD on a geospatial scale to prevent its spread. Using the Cellular Automata (CA)–Markov model, this study predicts the occurrence area [...] Read more.

Pine wilt disease (PWD) has become a devastating disease that impacts China’s forest management. It is of great significance to accurately predict PWD on a geospatial scale to prevent its spread. Using the Cellular Automata (CA)–Markov model, this study predicts the occurrence area of PWD in Anhui Province in 2030 based on PWD-relevant factors, such as weather, terrain, population, and traffic. Using spatial autocorrelation analysis, direction analysis and other spatial analysis methods, we analyze the change trend of occurrence data of PWD in 2000, 2010, 2020 and 2030, reveal the propagation law of PWD disasters in Anhui Province, and warn for future prevention and control direction and measures. The results show the following: (1) the overall accuracy of the CA–Markov model for PWD disaster prediction is 93.19%, in which the grid number accuracy is 95.19%, and the Kappa coefficient is 0.65. (2) In recent 20 years and the next 10 years, the occurrence area of PWD in Anhui Province has a trend of first decreasing and then increasing. From 2000 to 2010, the occurrence area of disasters has a downward trend. From 2010 to 2020, the disaster area has increased rapidly, with an annual growth rate of 140%. In the next 10 years, the annual growth rate of disasters will slow down, and the occurrence area of PWD will reach 270,632 ha. (3) In 2000 and 2010, the spatial aggregation and directional distribution characteristics of the map spots of the PWD pine forest were significant. In 2020 and 2030, the spatial aggregation is still significant after the expansion of the susceptible area, but the directional distribution is no longer significant. (4) The PWD center in Anhui Province shows a significant trend of moving southward. From 2010 to 2020, the PWD center moved from Chuzhou to Anqing. (5) PWD mainly occurs in the north slope area below 700 m above sea level and below 20° slope in Anhui Province. The prediction shows that the PWD disaster will break through the traditional suitable area in the next 10 years, and the distribution range will spread to high altitude, high slope, and sunny slope. The results of this study can provide scientific support for the prevention and control of PWD in the region and help the effective control of PWD in China. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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17 pages, 641 KiB  

Open AccessArticle

The Sustainable Development of Forest Food

by Weilung Huang, Si Chen, Xiaomei Zhang and Xuemeng Zhao

Sustainability 2022, 14(20), 13092; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013092 - 13 Oct 2022

Cited by 1 | Viewed by 1722

Abstract

This paper aims to study the sustainable development of forest food by exploring the input–output relationship of forest food value chains (FFVC) and its mediating effect on the integrity and agglomeration of FFVC. Through a literature review and interviews with experts, this paper [...] Read more.

This paper aims to study the sustainable development of forest food by exploring the input–output relationship of forest food value chains (FFVC) and its mediating effect on the integrity and agglomeration of FFVC. Through a literature review and interviews with experts, this paper included measurement variables, such as FFVC’s input, output, integrity, and agglomeration, and used PLS-SEM to study their relationships and the mediating effects of Chinese FFVC. The results showed that first, the measurement of FFVC’s integrity and agglomeration focused on FFVC’s rationality, development, comparative advantages, scale, space, network, and innovation; second, there was evidence of a significant input–output relationship of FFVC; third, there was a significant mediating effect of integrity and agglomeration of FFVC, which should be included in the government’s policies to promote FFVC; forth, Chinese FFVC is still at its infancy, and the government must implement FFVC sustainable development policies to promote the rationalization, upgrading, and spatial coupling of integrity and agglomeration of FFVC. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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16 pages, 2766 KiB  

Open AccessArticle

Evolutionary Game Analysis of the Quality of Agricultural Products in Supply Chain

by Feixiao Wang and Yaoqun Xu

Agriculture 2022, 12(10), 1575; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12101575 - 29 Sep 2022

Cited by 14 | Viewed by 1977

Abstract

There are many factors affecting the quality and safety of agricultural products in the supply chain of agricultural products. In order to ensure the quality and safety of agricultural products, suppliers and processors need to take their own quality measures to ensure the [...] Read more.

There are many factors affecting the quality and safety of agricultural products in the supply chain of agricultural products. In order to ensure the quality and safety of agricultural products, suppliers and processors need to take their own quality measures to ensure the quality of agricultural products. Quality inspection departments need to strictly supervise suppliers and processors to ensure the implementation of quality measures by both parties. Within the supply chain, the decisions of these three stakeholders are affected by the initial intention, the cost of quality measures, and the penalty amount of the quality inspection department. Outside the supply chain, they are affected by government regulation and consumer feedback. This paper takes the stakeholders in the agricultural product supply chain as the object, brings suppliers, processors, and quality inspection departments into the evolutionary game model, brings the factors that affect the decision-making of these three stakeholders into the model as parameters to analyze the stability of the model in different situations, and then analyzes the factors that affect the decision-making of stakeholders through mathematical simulation according to specific examples. The results show that the enthusiasm of stakeholders to ensure the quality of agricultural products is most affected by the initial intention of each other and the cost of quality measures. At the same time, the punishment of the quality inspection department, the feedback of consumers, and the supervision of the government also play a good role in promoting quality. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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19 pages, 6152 KiB  

Open AccessReview

Forestry Big Data: A Review and Bibliometric Analysis

by Wen Gao, Quan Qiu, Changyan Yuan, Xin Shen, Fuliang Cao, Guibin Wang and Guangyu Wang

Forests 2022, 13(10), 1549; https://0-doi-org.brum.beds.ac.uk/10.3390/f13101549 - 22 Sep 2022

Cited by 9 | Viewed by 2379

Abstract

Due to improved data collection and processing techniques, forestry surveys are now more efficient and accurate, generating large amounts of forestry data. Forestry Big Data (FBD) has become a critical component of the forestry inventory investigation system. In this study, publications on FBD [...] Read more.

Due to improved data collection and processing techniques, forestry surveys are now more efficient and accurate, generating large amounts of forestry data. Forestry Big Data (FBD) has become a critical component of the forestry inventory investigation system. In this study, publications on FBD were identified via the Web of Science database, and a comprehensive bibliometric analysis, network analysis, and analysis of major research streams were conducted to present an overview of the FBD field. The results show that FBD research only began nearly a decade ago but has undergone an upswing since 2016. The studies were mainly conducted by China and the US, and collaboration among authors is relatively fragmented. FBD research involved interdisciplinary integration. Among all the keywords, data acquisition (data mining and remote sensing) and data processing (machine learning and deep learning) received more attention, while FBD applications (forecasting, biodiversity, and climate change) have only recently received attention. Our research reveals that the FBD research is still in the infancy stage but has grown rapidly in recent years. Data acquisition and data processing are the main research fields, whereas FBD applications have gradually emerged and may become the next focus. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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15 pages, 3100 KiB  

Open AccessArticle

Detection of Eucalyptus Leaf Disease with UAV Multispectral Imagery

by Kuo Liao, Fan Yang, Haofei Dang, Yunzhong Wu, Kunfa Luo and Guiying Li

Forests 2022, 13(8), 1322; https://0-doi-org.brum.beds.ac.uk/10.3390/f13081322 - 18 Aug 2022

Cited by 2 | Viewed by 2694

Abstract

Forest disease is one of the most important factors affecting tree growth and product quality, reducing economic values of forest ecosystem goods and services. In order to prevent and control forest diseases, accurate detection in a timely manner is essential. Unmanned aerial vehicles [...] Read more.

Forest disease is one of the most important factors affecting tree growth and product quality, reducing economic values of forest ecosystem goods and services. In order to prevent and control forest diseases, accurate detection in a timely manner is essential. Unmanned aerial vehicles (UAVs) are becoming an important tool for acquiring multispectral imagery, but have not been extensively used for detection of forest diseases. This research project selected a eucalyptus forest as a case study to explore the performance of leaf disease detection using high spatial resolution multispectral imagery that had been acquired by UAVs. The key variables sensitive to eucalyptus leaf diseases, including spectral bands and vegetation indices, were identified by using a mutual information–based feature selection method, then distinguishing disease levels using random forest and spectral angle mapper approaches. The results show that green, red edge, and near-infrared wavelengths, nitrogen reflectance index, and greenness index are sensitive to forest diseases. The random forest classifier, based on a combination of sensitive spectral bands (green, red edge, and near-infrared wavelengths) and a nitrogen reflectance index, provided the best differentiation results for healthy and three disease severity levels (mild, moderate, and severe) with overall accuracy of 90.1% and kappa coefficient of 0.87. This research provides a new way to detect eucalyptus leaf diseases, and the proposed method may be suitable for other forest types. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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Supplementary material:
Supplementary File 1 (ZIP, 344 KiB)

20 pages, 3214 KiB  

Open AccessArticle

Blue and Green Water Footprint of Agro-Industrial Avocado Production in Central Mexico

by Alberto F. Gómez-Tagle, Alberto Gómez-Tagle, Diana J. Fuerte-Velázquez, Alma G. Barajas-Alcalá, Fernando Quiroz-Rivera, Pablo E. Alarcón-Chaires and Hilda Guerrero-García-Rojas

Sustainability 2022, 14(15), 9664; https://0-doi-org.brum.beds.ac.uk/10.3390/su14159664 - 05 Aug 2022

Cited by 4 | Viewed by 3916

Abstract

Mexico is the world-leading avocado producer. The municipality of Uruapan in the Avocado Belt region in Central Mexico produces 153,000 tons a year, nearly 6.4% of Mexico’s total volume. We performed a green and blue water footprint (WF) analysis between 2012 to 2017 [...] Read more.

Mexico is the world-leading avocado producer. The municipality of Uruapan in the Avocado Belt region in Central Mexico produces 153,000 tons a year, nearly 6.4% of Mexico’s total volume. We performed a green and blue water footprint (WF) analysis between 2012 to 2017 in this municipality, and compared the estimated WF volumes with water concessions for agriculture. Mean annual rainfall was 1757.0 mm in the study period, mean effective rainfall 877.2 mm, mean crop evapotranspiration 933.1 mm, and 312.5 mm of mean irrigation requirement. The mean WFtotal was 744.3 m³ ton⁻¹, below the global mean WF for this crop (1086 m³ ton⁻¹). WFtotal was 2.5 times higher in irrigated plantations (1071.4 m3 ton⁻1) than in rainfed plantations (417.1 m³ ton⁻¹). The crop yield was slightly higher (3.8%) under irrigated (10.26 ton ha⁻¹ year⁻¹) than in rainfed plantations (9.88 ton ha⁻¹ year⁻¹). WF and its components varied between years. The lowest WFblue was in 2015 when atypical spring rainfall increased available water during the dry season. The irrigation of avocado plantations doubles water use with a slight yield increase in relation to rainfed plantations. Regarding WF volumes and water concessions, we found that agroindustrial avocado production consumes up to 120% of the surface and groundwater volumes granted to agriculture use in years with dry conditions. The results indicate that other water users are depleted of this resource, creating water stress and scarcity, and leading to water rights conflicts and social discomfort. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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19 pages, 5728 KiB  

Open AccessArticle

Monitoring Key Wheat Growth Variables by Integrating Phenology and UAV Multispectral Imagery Data into Random Forest Model

by Shaoyu Han, Yu Zhao, Jinpeng Cheng, Fa Zhao, Hao Yang, Haikuan Feng, Zhenhai Li, Xinming Ma, Chunjiang Zhao and Guijun Yang

Remote Sens. 2022, 14(15), 3723; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153723 - 03 Aug 2022

Cited by 18 | Viewed by 2615

Abstract

Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models [...] Read more.

Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok’s scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R² from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R² > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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16 pages, 311 KiB  

Open AccessArticle

Impact of Agricultural Extension Services on Fertilizer Use and Farmers’ Welfare: Evidence from Bangladesh

by Mohammad Mahbubur Rahman and Jeffry D. Connor

Sustainability 2022, 14(15), 9385; https://0-doi-org.brum.beds.ac.uk/10.3390/su14159385 - 31 Jul 2022

Cited by 12 | Viewed by 2608

Abstract

Although many studies have assessed the impact of extension, most treat the presence or absence of extension as a binary variable to test treatment effects, and fewer investigate how the type of provider (e.g., govt./private) and the frequency of the contact (number of [...] Read more.

Although many studies have assessed the impact of extension, most treat the presence or absence of extension as a binary variable to test treatment effects, and fewer investigate how the type of provider (e.g., govt./private) and the frequency of the contact (number of extension visits) impact farm household welfare. To address this knowledge gap, this article investigates the impact of agricultural extension access, frequency, and provider type on chemical fertilizer application, crop yield, and profit. Data from a nationwide survey in 2015 in Bangladesh, a case country with a heavy over-application of urea fertilizer, are the basis for the endogenous switching regression approach to control for potential self-selection and endogeneity. The empirical results revealed significant differences in the outcomes for farmers who had just one extension contact, more than one extension contact, and those who accessed private provisions. We found that farmers who frequently accessed extension used significantly less urea fertilizer than farmers who accessed extension only once. Farmers who accessed extension more frequently also experienced a statistically significantly higher yield and profit from cropping. Private extension access appeared to result in statistically significantly higher incomes but not reduced urea fertilizer application rates. Our results suggest that a more nuanced understanding can be gained from extension source and frequency treatment effects modelling than with the presence or absence of the extension binary variable formulation that is most common in the literature. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

17 pages, 5801 KiB  

Open AccessEditor’s ChoiceArticle

Towards Continuous Stem Water Content and Sap Flux Density Monitoring: IoT-Based Solution for Detecting Changes in Stem Water Dynamics

by Shahla Asgharinia, Martin Leberecht, Luca Belelli Marchesini, Nicolas Friess, Damiano Gianelle, Thomas Nauss, Lars Opgenoorth, Jim Yates and Riccardo Valentini

Forests 2022, 13(7), 1040; https://0-doi-org.brum.beds.ac.uk/10.3390/f13071040 - 01 Jul 2022

Cited by 7 | Viewed by 2845

Abstract

Taking advantage of novel IoT technologies, a new multifunctional device, the “TreeTalker”, was developed to monitor real-time ecophysical and biological parameters of individual trees, as well as climatic variables related to their surrounding environment, principally, air temperature and air relative humidity. Here, IoT [...] Read more.

Taking advantage of novel IoT technologies, a new multifunctional device, the “TreeTalker”, was developed to monitor real-time ecophysical and biological parameters of individual trees, as well as climatic variables related to their surrounding environment, principally, air temperature and air relative humidity. Here, IoT applied to plant ecophysiology and hydrology aims to unravel the vulnerability of trees to climatic stress via a single tree assessment at costs that enable massive deployment. We present the performance of the TreeTalker to elucidate the functional relation between the stem water content in trees and respective internal/external (stem hydraulic activity/abiotic) drivers. Continuous stem water content records are provided by an in-house-designed capacitance sensor, hosted in the reference probe of the TreeTalker sap flow measuring system, based on the transient thermal dissipation (TTD) method. In order to demonstrate the capability of the TreeTalker, a three-phase experimental process was performed including (1) sensor sensitivity analysis, (2) sensor calibration, and (3) long-term field data monitoring. A negative linear correlation was demonstrated under temperature sensitivity analysis, and for calibration, multiple linear regression was applied on harvested field samples, explaining the relationship between the sample volumetric water content and the sensor output signal. Furthermore, in a field scenario, TreeTalkers were mounted on adult Fagus sylvatica L. and Quercus petraea L. trees, from June 2020 to October 2021, in a beech-dominated forest near Marburg, Germany, where they continuously monitored sap flux density and stem volumetric water content (stem VWC). The results show that the range of stem VWC registered is highly influenced by the seasonal variability of climatic conditions. Depending on tree characteristics, edaphic and microclimatic conditions, variations in stem VWC and reactions to atmospheric events occurred. Low sapwood water storage occurs in response to drought, which illustrates the high dependency of trees on stem VWC under water stress. Consistent daily variations in stem VWC were also clearly detectable. Stem VWC constitutes a significant portion of daily transpiration (using TreeTalkers, up to 4% for the beech forest in our experimental site). The diurnal–nocturnal pattern of stem VWC and sap flow revealed an inverse relationship. Such a finding, still under investigation, may be explained by the importance of water recharge during the night, likely due to sapwood volume changes and lateral water distribution rather than by a vertical flow rate. Overall, TreeTalker demonstrated the potential of autonomous devices for monitoring sap density and relative stem VWC in the field of plant ecophysiology and hydrology. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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21 pages, 8629 KiB  

Open AccessArticle

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

by Yingbo Li, Guoqi Chai, Yueting Wang, Lingting Lei and Xiaoli Zhang

Remote Sens. 2022, 14(13), 3035; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133035 - 24 Jun 2022

Cited by 27 | Viewed by 2819

Abstract

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation [...] Read more.

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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20 pages, 47640 KiB  

Open AccessArticle

UAV-LiDAR Measurement of Vegetation Canopy Structure Parameters and Their Impact on Land–Air Exchange Simulation Based on Noah-MP Model

by Guotong Wu, Yingchang You, Yibin Yang, Jiachen Cao, Yujie Bai, Shengjie Zhu, Liping Wu, Weiwen Wang, Ming Chang and Xuemei Wang

Remote Sens. 2022, 14(13), 2998; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132998 - 23 Jun 2022

Cited by 2 | Viewed by 1830

Abstract

Land surface processes play a vital role in the exchange of momentum, energy, and mass between the land and the atmosphere. However, the current model simplifies the canopy structure using approximately three to six parameters, which makes the representation of canopy radiation and [...] Read more.

Land surface processes play a vital role in the exchange of momentum, energy, and mass between the land and the atmosphere. However, the current model simplifies the canopy structure using approximately three to six parameters, which makes the representation of canopy radiation and energy distribution uncertain to a large extent. To improve the simulation performance, more specific canopy structure parameters were retrieved by a UAV-LiDAR observation system and updated into the multiparameterization version of the Noah land surface model (Noah-MP) for a typical forest area. Compared with visible-light photogrammetry, LiDAR retrieved a more accurate vertical canopy structure, which had a significant impact on land–air exchange simulations. The LiDAR solution resulted in a 35.0∼48.0% reduction in the range of perturbations for temperature and another 27.8% reduction in the range of perturbations for moisture. This was due to the canopy structure affecting the radiation and heat fluxes of the forest, reducing their perturbation range by 7.5% to 30.1%. To reduce the bias of the land surface interaction simulation, it will be necessary to improve the method of retrieving the canopy morphological parameterization through UAV-LiDAR on a continued basis in the future. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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21 pages, 25803 KiB  

Open AccessArticle

Near-Earth Remote Sensing Images Used to Determine the Phenological Characteristics of the Canopy of Populus tomentosa B301 under Three Methods of Irrigation

by Peng Guan, Yili Zheng, Guannan Lei, Yang Liu, Lichen Zhu, Youzheng Guo, Yirui Wang and Benye Xi

Remote Sens. 2022, 14(12), 2844; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122844 - 14 Jun 2022

Cited by 3 | Viewed by 1844

Abstract

Due to global warming, changes in plant phenology such as an early leaf spreading period in spring, a late abscission period in autumn, and growing season extension are commonly seen. Here, near-earth remote sensing images were used to monitor the canopy phenology of [...] Read more.

Due to global warming, changes in plant phenology such as an early leaf spreading period in spring, a late abscission period in autumn, and growing season extension are commonly seen. Here, near-earth remote sensing images were used to monitor the canopy phenology of Populus tomentosa B301 in planted forests under full drip irrigation, full furrow irrigation, and no irrigation (rain fed). Experiments were conducted to collect phenological data across a growing season. Continuous canopy images were used to calculate different vegetation indices; the key phenological period was determined via the double logistic model and the curvature method. The effects of irrigation methods and precipitation in the rainy season on tree growth changes and key phenological periods were analyzed. The results showed that: (1) The green chromatic coordinate (GCC) conformed to the vegetation index of the tree species canopy phenological study. (2) During the phenological period throughout the year, the GCC reaching peak time (MOE) of the canopy phenology of Populus tomentosa B301 was the same in the three methods, while the time of shedding at the end of the growing season without irrigation (preset point 1) was 8 days longer than with full drip irrigation (preset point 3), and 7 days faster than with full furrow irrigation (preset point 5). (3) In the preliminary rainy season, different irrigation volumes induced different growth changes and phenological periods of the trees, resulting in different data of vegetation indicators under different growth conditions. (4) During the rainy season, the precipitation had different effects on cultivating P. tomentosa B301 using the three methods, that is, high precipitation could increase the growth rate of the fully irrigated area, otherwise the growth rate of this tree species was increased in full drip irrigation areas. Precipitation was lower and irregular, and the growth rate of this species was faster than the other two irrigation methods in the non-irrigated area, which was more adaptable to external environmental changes. The internal growth mechanism of the phenological changes in different areas of the planted forests was influenced by the different cultivation methods. Moreover, the collected phenological data provide a basis for the study of plant phenology with large data sets and deepens our understanding of the phenology of planted forests in response to climate change. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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17 pages, 5030 KiB  

Open AccessArticle

Evaluation of the Moso Bamboo Age Determination Based on Laser Echo Intensity

by Wenbing Xu, Zihao Fang, Suying Fan and Susu Deng

Remote Sens. 2022, 14(11), 2550; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112550 - 26 May 2022

Viewed by 1662

Abstract

Determination of bamboo age is an important task for bamboo forest management and bamboo utilization. However, the bamboo age is usually manually determined in the field, which is time-consuming and labor-intensive. Due to the ability to generate very high-density point clouds, terrestrial laser [...] Read more.

Determination of bamboo age is an important task for bamboo forest management and bamboo utilization. However, the bamboo age is usually manually determined in the field, which is time-consuming and labor-intensive. Due to the ability to generate very high-density point clouds, terrestrial laser scanning (TLS) has been applied in forestry to acquire forest parameters. This study evaluated the potential of using the laser echo intensity data generated by TLS technology to determine the Moso bamboo age represented by “du.” The intensity data were first corrected for the distance and incidence angle effects using an intensity correction method that constructed an empirical correction model by fitting piecewise polynomials to the intensity data collected based on a reference target. Then the models expressing the relationship between intensity and bamboo culm section number were constructed for different bamboo du by fitting polynomials to the intensity data of individual bamboo culms through least-squares adjustment. For a bamboo plant whose age is determined, the bamboo du could be determined based on the constructed intensity-culm section models. The proposed bamboo age determination method was tested at a site in a managed Moso bamboo forest in Lin’an District, Hangzhou City, Zhejiang Province, China. From the test site, 56 and 120 bamboo plants with known bamboo ages were selected to construct the intensity-culm section models and to validate the bamboo age determination method, respectively. The bamboo age determination accuracies for each bamboo du were all above 90%. The result indicates a great potential for automatic determination of bamboo age in practice using TLS technology. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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15 pages, 4148 KiB  

Open AccessArticle

Nondestructive Detection Method for the Calcium and Nitrogen Content of Living Plants Based on Convolutional Neural Networks (CNN) Using Multispectral Images

by Grzegorz Kunstman, Paweł Kunstman, Łukasz Lasyk, Jacek Stanisław Nowak, Agnieszka Stępowska, Waldemar Kowalczyk, Jakub Dybaś and Ewa Szczęsny-Małysiak

Agriculture 2022, 12(6), 747; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12060747 - 25 May 2022

Cited by 1 | Viewed by 2030

Abstract

Herein, we present the novel method targeted for determination of plant nutritional state with the use of computer vision and Neural Networks. The method is based on multispectral imaging performed by an exclusively designed Agroscanner and a dedicated analytical system for further data [...] Read more.

Herein, we present the novel method targeted for determination of plant nutritional state with the use of computer vision and Neural Networks. The method is based on multispectral imaging performed by an exclusively designed Agroscanner and a dedicated analytical system for further data analysis with Neural Networks. An Agroscanner is a low-cost mobile construction intended for multispectral measurements at macro-scale, operating at four wavelengths: 470, 550, 640 and 850 nm. Together with developed software and implementation of a Neural Network it was possible to design a unique approach to process acquired plant images and assess information about plant physiological state. The novelty of the developed technology is focused on the multispectral, macro-scale analysis of individual plant leaves, rather than entire fields. Such an approach makes the method highly sensitive and precise. The method presented herein determines the basic physiological deficiencies of crops with around 80% efficiency. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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16 pages, 3325 KiB  

Open AccessArticle

Specifying Spatial Dependence for Teak Stands Specific to Solomon Island-Derived Clones in Tawau, Sabah, Malaysia: A Preliminary Study

by Johannah Jamalul Kiram, Rossita Mohamad Yunus, Yani Japarudin and Mahadir Lapammu

Sustainability 2022, 14(10), 6005; https://0-doi-org.brum.beds.ac.uk/10.3390/su14106005 - 15 May 2022

Cited by 1 | Viewed by 1489

Abstract

The magnitude of spatial dependence on teak tree growth was examined based on a teak plantation managed by the research and development team at Sabah Softwood Berhad, Brumas camp, Tawau, Sabah, Malaysia. A sample of 432 and 445 georeferenced individual tree points specific [...] Read more.

The magnitude of spatial dependence on teak tree growth was examined based on a teak plantation managed by the research and development team at Sabah Softwood Berhad, Brumas camp, Tawau, Sabah, Malaysia. A sample of 432 and 445 georeferenced individual tree points specific to Solomon Island-derived clones that were 6 and 7 years old, respectively, were analyzed, as previous findings showed that this was the genotype that thrived the most. This study aims to show that spatial dependence exists in the 6- and 7-year-old teak tree blocks of the plantation and that there are changes in the magnitude of spatial dependence when it is analyzed as a continuous plot. Moran’s I values and Moran scatterplots as well as semivariograms and thematic maps were used to satisfy the hypothesis regarding the relationship between spatial dependence and the growth of the physical parameters: the diameter at breast height (DBH), height, and the volume of the teak tree. The Moran’s I values that were calculated rejected the null hypothesis, suggesting the existence of strong spatial dependence for all of the physical parameters and for both the 6- and 7-year-old samples. The semivariograms were plotted and showed an increasing trend as the lag distance between trees increased and showed changes as the trees aged. These findings prove significant spatial dependence in the growth of the physical parameters of teak trees. Hence, growth model methodologies based on spatial distribution must be developed to further understand the spatial distribution of teak tree plantations. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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21 pages, 3253 KiB  

Open AccessArticle

The Collaboration Mechanism of Agricultural Product Supply Chain Dominated by Farmer Cooperatives

by Yujia Huo, Jiali Wang, Xiangyu Guo and Yang Xu

Sustainability 2022, 14(10), 5824; https://0-doi-org.brum.beds.ac.uk/10.3390/su14105824 - 11 May 2022

Cited by 11 | Viewed by 2160

Abstract

Problems such as the reduction of the added value of agricultural products and the interruption of the supply of agricultural products caused by the unstable collaborative relationship have seriously hindered the high-quality development of the agricultural product supply chain. Promoting the stable collaboration [...] Read more.

Problems such as the reduction of the added value of agricultural products and the interruption of the supply of agricultural products caused by the unstable collaborative relationship have seriously hindered the high-quality development of the agricultural product supply chain. Promoting the stable collaboration in the agricultural product supply chain is an urgent problem. Considering the characteristic demand of consumers for agricultural products, this paper takes the supply chain mainly operating characteristic agricultural products and dominated by farmer cooperatives as the research object and constructs a tripartite evolutionary game model of farmer cooperatives, manufacturers, and retailers. We study the supply chain collaboration mechanism from the main strategy choice and the specific factors affecting its strategy choice. The results show that farmer cooperatives implement a strict supervision strategy and increase the reward and punishment to promote the collaboration in the supply chain, but the increase in supervision cost is not conducive to the income of farmer cooperatives. In the case of loose supervision, the difference between the additional income and the collaboration input is higher than the “free-rider” income obtained when adopting a non-collaboration strategy, which is conducive to its evolution towards collaboration. In addition, increasing additional income, improving synergy coefficient, and reducing collaboration input and “free-rider” income will increase the probability of the system evolving to Pareto optimal, and accelerate the realization of comprehensive collaboration in the agricultural product supply chain dominated by farmer cooperatives. The research results provide a certain supplement to the related research on agricultural product supply chains in theory, and provide a reference for the comprehensive collaboration of the agricultural product supply chain dominated by farmer cooperatives in practice. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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19 pages, 1958 KiB  

Open AccessArticle

Solar Radiation Prediction Model for the Yellow River Basin with Deep Learning

by Qian Zhang, Xiaoxu Tian, Peng Zhang, Lei Hou, Zhigong Peng and Gang Wang

Agronomy 2022, 12(5), 1081; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12051081 - 29 Apr 2022

Cited by 4 | Viewed by 1485

Abstract

Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the [...] Read more.

Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the highest temperature T_max, lowest temperature T_min, average temperature T_avg, wind speed U, relative humidity RH, sunshine duration H and maximum sunshine duration H_max as input variables to construct a deep learning prediction model of solar radiation in the Yellow River Basin. It is compared with the recommended and corrected values of the widely used Å-P method. The results show that: (1) The correction results of the Å-P equation are better in the upstream and downstream of the Yellow River Basin but worse in the midstream. (2) The prediction result of the deep learning model in the Yellow River Basin is far better than that of the Å-P equation using the FAO-56 recommended value. It is the best in the downstream of the Yellow River Basin: R² increases from 0.894 to 0.934; MSE, RMSE and MAE decrease by 43.12%, 27.73% and 25.80%, respectively. The upstream prediction result comes in second: R² increases from 0.888 to 0.921; MSE, RMSE and MAE decrease by 33.27%, 20.02% and 19.04%, respectively. The midstream result is the worst: R² increases from 0.869 to 0.874; MSE, RMSE and MAE decrease by −0.50%, 0.07% and 3.82%, respectively. (3) The prediction results of the deep learning model in the upstream and downstream of the Yellow River Basin are far better than those of the Å-P equation using correction. The R² in the upstream of the Yellow River Basin increases from 0.889 to 0.921. MSE, RMSE and MAE decrease by 22.11%, 11.84% and 8.94%, respectively. R² in the downstream of the Yellow River Basin increases from 0.900 to 0.934, and MSE, RMSE and MAE decrease by 13.21%, 11.40% and 5.55%, respectively. In the midstream of the Yellow River Basin, the prediction results of the deep learning model are worse than those of the Å-P equation using correction: R² increases from 0.870 to 0.874, but MSE, RMSE and MAE decrease by −24.93%, −10.83% and −11.56%, respectively. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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13 pages, 1208 KiB  

Open AccessArticle

Development of Sustainable Production of Rainfed Winter Wheat with No-Till Technologies in Southern Kazakhstan

by Sagadat Turebayeva, Aigul Zhapparova, Gulnur Kekilbayeva, Sayagul Kenzhegulova, Khaiyrnisa Aisakulova, Gainiya Yesseyeva, Anuarbek Bissembayev, Biljana Sikirić, Dossymbek Sydyk and Elmira Saljnikov

Agronomy 2022, 12(4), 950; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12040950 - 15 Apr 2022

Cited by 3 | Viewed by 2392

Abstract

The production of rainfed crops in arid regions is an extremely difficult task, especially without tillage. In southern Kazakhstan, in 2020–2021, the approbation of various nutrition regimes for winter wheat grown in conditions of no-tillage rainfed lands has been studied. The effect of [...] Read more.

The production of rainfed crops in arid regions is an extremely difficult task, especially without tillage. In southern Kazakhstan, in 2020–2021, the approbation of various nutrition regimes for winter wheat grown in conditions of no-tillage rainfed lands has been studied. The effect of different doses and terms of application of growth stimulators, micronutrients, bio-fertilizers and mineral fertilizers, as well as their economic efficiency, was studied in ten variables. The use of a combination of growth stimulators and microfertilizers produced the highest grain yield and was the most cost-effective. The greatest value of the nominal net profit of 223.25 euro and 244.10 euro from one hectare was provided and calculated with the recommended target grain yield of 2.0 t/ha dose of mineral fertilizers, respectively; however, the production cost of one ton of grain in these treatments was also highest. Further research is continuing with a wider range and combination of amendments and various crops in a rainfed no-till winter wheat farm in southern Kazakhstan. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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22 pages, 2104 KiB  

Open AccessArticle

Potentiality of Formulated Bioagents from Lab to Field: A Sustainable Alternative for Minimizing the Use of Chemical Fungicide in Controlling Potato Late Blight

by Md. Huzzatul Islam, Md. Mostafa Masud, Muhtarima Jannat, Muhammad Iqbal Hossain, Shafiqul Islam, Md. Zahangir Alam, Francois J. B. Serneels and Md. Rashidul Islam

Sustainability 2022, 14(8), 4383; https://0-doi-org.brum.beds.ac.uk/10.3390/su14084383 - 07 Apr 2022

Cited by 4 | Viewed by 2358

Abstract

Late blight of potato caused by an oomycete, Phytophthora infestans (Mont.) De Bary limits the production of potato worldwide. Late blight management has been based on chemical fungicide application, and the repeated use of these fungicides introduces new and more aggressive genotypes, [...] Read more.

Late blight of potato caused by an oomycete, Phytophthora infestans (Mont.) De Bary limits the production of potato worldwide. Late blight management has been based on chemical fungicide application, and the repeated use of these fungicides introduces new and more aggressive genotypes, which can rapidly overcome host resistance. Therefore, innovative and effective control measures are needed if fungicide use is to be reduced or eliminated. Some potential formulated bacterial bioagents viz. Pseudomonas putida (BDISO64RanP) and Bacillus subtilis (BDISO36ThaR), and fungal bioagents viz. Trichoderma paraviridicens (BDISOF67R) and T. erinaceum (BDISOF91R), were evaluated for their performance in controlling late blight of potato under growth chamber and field conditions. Both artificial inoculation and field experiments revealed that eight sprays of these bacterial (P. putida and B. subtilis) and fungal (T. erinaceum) bioagents were found to be most effective at reducing late blight severity by 99% up until 60 days after planting (DAP), whereas these bioagents were found to be partially effective until 70 DAP, reducing late blight severity by 46 to 60% and 58 to 60% in the field and growth chamber conditions, respectively. However, these bioagents can reduce the spray frequencies of Curzate M8 by 50% (four sprays instead of eight) when applied together with this fungicide. Economic analysis revealed that T₆ (eight sprays of formulated P. putida + B. subtilis + four sprays of Curzate M8) and T₁₆ (eight sprays of formulated P. putida, B. subtilis, and T. erinaceum + four sprays of Curzate M8) performed better in consecutive two years, applying less fungicidal spray compared to T₁ (eight sprays of Curzate M8 (Positive control)), which indicated that the return ranged, by Bangladeshi Currency (Taka), from 0.85 to 0.90 over the investment of Bangladeshi Currency (Taka) 1.00 in these treatments, and these results together highlight the possibility of using bioagents in reducing late blight of potato under a proper warning system to reduce the application frequency of chemical fungicide. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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17 pages, 5561 KiB  

Open AccessArticle

Algorithm for Extracting the 3D Pose Information of Hyphantria cunea (Drury) with Monocular Vision

by Meixiang Chen, Ruirui Zhang, Meng Han, Tongchuan Yi, Gang Xu, Lili Ren and Liping Chen

Agriculture 2022, 12(4), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12040507 - 02 Apr 2022

Viewed by 1789

Abstract

Currently, the robustness of pest recognition algorithms based on sample augmentation with two-dimensional images is negatively affected by moth pests with different postures. Obtaining three-dimensional (3D) posture information of pests can provide information for 3D model deformation and generate training samples for deep [...] Read more.

Currently, the robustness of pest recognition algorithms based on sample augmentation with two-dimensional images is negatively affected by moth pests with different postures. Obtaining three-dimensional (3D) posture information of pests can provide information for 3D model deformation and generate training samples for deep learning models. In this study, an algorithm of the 3D posture information extraction method for Hyphantria cunea (Drury) based on monocular vision is proposed. Four images of every collected sample of H. cunea were taken at 90° intervals. The 3D pose information of the wings was extracted using boundary tracking, edge fitting, precise positioning and matching, and calculation. The 3D posture information of the torso was obtained by edge extraction and curve fitting. Finally, the 3D posture information of the wings and abdomen obtained by this method was compared with that obtained by Metrology-grade 3D scanner measurement. The results showed that the relative error of the wing angle was between 0.32% and 3.03%, the root mean square error was 1.9363, and the average relative error of the torso was 2.77%. The 3D posture information of H. cunea can provide important data support for sample augmentation and species identification of moth pests. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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18 pages, 4633 KiB  

Open AccessArticle

Prediction Model and Influencing Factors of CO₂ Micro/Nanobubble Release Based on ARIMA-BPNN

by Bingbing Wang, Xiangjie Lu, Yanzhao Ren, Sha Tao and Wanlin Gao

Agriculture 2022, 12(4), 445; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12040445 - 23 Mar 2022

Cited by 2 | Viewed by 1706

Abstract

The quantitative prediction of CO₂ concentration in the growth environment of crops is a key technology for CO₂ enrichment applications. The characteristics of micro/nanobubbles in water make CO₂ micro/nanobubble water potentially useful for enriching CO₂ during growth of crops. [...] Read more.

The quantitative prediction of CO₂ concentration in the growth environment of crops is a key technology for CO₂ enrichment applications. The characteristics of micro/nanobubbles in water make CO₂ micro/nanobubble water potentially useful for enriching CO₂ during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO₂ micro/nanobubbles. In this paper, the factors influencing CO₂ release and changes in CO₂ concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO₂ concentration and various influencing factors within a time series is proposed to predict the released CO₂ concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R² value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO₂ micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO₂ micro/nanobubble water in agriculture. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry)

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