Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume

Abstract submission deadline

31 May 2024

Manuscript submission deadline

31 August 2024

Viewed by

9912

Topic Information

Dear Colleagues,

As agriculture and forestry enter the digital era, the digital technology related to these topics has started to become widespread. This is referred to as 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 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 cost reduction, quality improvements and increased efficiency. The rapid development of artificial intelligence, UAV, remote sensing, big data analysis and other technologies provides a substantial support for agriculture and forestry to move forward to the intelligent era.

We are pleased to announce the opening of Volume II, “Challenges, Development and Frontiers of Smart Agriculture and Forestry-II”, which will follow on from Volume I. In this second edition, we aim to fully explore frontier technologies, including the internet of things, multi-modal and multi-angle stereo observation, cloud computing, big data, machine learning, intelligent decision-making, etc., thereby exploring their application potential in smart agriculture and forestry and accelerating the development of precision agriculture and forestry.

This Topic welcomes high-quality works that focus on the innovation of five core technologies, including remote sensing, advanced sensors, big data and cloud services, precision operation technology and equipment, internet of things and robots, as well as 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	Submit
Agronomy agronomy	3.7	5.2	2011	15.8 Days	CHF 2600	Submit
Forests forests	2.9	4.5	2010	16.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Related Topic

Published Papers (7 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Agriculture Agronomy Forests Remote Sensing Sustainability

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

20 pages, 37741 KiB  

Open AccessArticle

SA-Pmnet: Utilizing Close-Range Photogrammetry Combined with Image Enhancement and Self-Attention Mechanisms for 3D Reconstruction of Forests

by Xuanhao Yan, Guoqi Chai, Xinyi Han, Lingting Lei, Geng Wang, Xiang Jia and Xiaoli Zhang

Remote Sens. 2024, 16(2), 416; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16020416 - 21 Jan 2024

Viewed by 1027

Abstract

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models [...] Read more.

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median–Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

19 pages, 5108 KiB  

Open AccessArticle

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

by Zongqi Yao, Guoqi Chai, Lingting Lei, Xiang Jia and Xiaoli Zhang

Remote Sens. 2023, 15(21), 5164; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15215164 - 29 Oct 2023

Viewed by 981

Abstract

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and [...] Read more.

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m² and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

attachment

Supplementary material:
Supplementary File 1 (ZIP, 5886 KiB)

18 pages, 9604 KiB  

Open AccessArticle

Integrating Agricultural and Ecotourism Development: A Crop Cultivation Suitability Framework Considering Tourists’ Landscape Preferences in Qinghai Province, China

by Huihui Wang, Jinyan Zhan, Chao Wang, Oleg Anatolyevich Blinov, Michael Asiedu Kumi, Wei Liu, Xi Chu, Yanmin Teng, Huizi Liu, Zheng Yang and Chunyue Bai

Remote Sens. 2023, 15(19), 4685; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15194685 - 25 Sep 2023

Cited by 1 | Viewed by 1260

Abstract

Ecotourism and agricultural development have been proven to have synergistic effects, although few studies have employed a spatial planning approach to incorporate tourism growth into crop cultivation planning. This study constructed a theoretical framework of environmental suitability—farmland accessibility—tourist’s landscape preferences for crop cultivation [...] Read more.

Ecotourism and agricultural development have been proven to have synergistic effects, although few studies have employed a spatial planning approach to incorporate tourism growth into crop cultivation planning. This study constructed a theoretical framework of environmental suitability—farmland accessibility—tourist’s landscape preferences for crop cultivation planning to link regional agriculture and ecotourism development. The spatial planning of rapeseed cultivation in Qinghai Province was chosen as a case study. The main research methods include an environmental suitability analysis based on remote sensing and Maxent modeling, a farmland accessibility analysis based on a GIS platform, and a landscape preference questionnaire survey of tourists. According to the survey’s findings, almost 80% of tourists thought rapeseed flowers enhanced the beauty of natural landscapes. This demonstrated the enormous potential of rapeseed fields for fostering ecotourism. Based on environmental factors, the optimum region for rapeseed cultivation covered 5.38% of the study area, or roughly 6327 km². The comprehensive optimum zone, which encompassed both agricultural accessibility and environmental suitability, was equal to 12.63% of the study area’s farming area, or around 929 km². This study’s crop cultivation suitability framework can integrate agricultural and ecotourism development, with substantial implications for achieving coordinated economic, social, and environmental development. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

23 pages, 3329 KiB  

Open AccessArticle

The Impact of Digital Economy Empowerment on Green Total Factor Productivity in Forestry

by Hanting Chen, Zhuoya Ma, Hui Xiao, Jing Li and Wenhui Chen

Forests 2023, 14(9), 1729; https://0-doi-org.brum.beds.ac.uk/10.3390/f14091729 - 27 Aug 2023

Cited by 2 | Viewed by 1054

Abstract

The digital economy is an important engine for promoting green economic development, and the integration of the digital and real economies can accelerate the transformation of the real economy. In order to explore the multifaceted influence of digital economy on forestry green total [...] Read more.

The digital economy is an important engine for promoting green economic development, and the integration of the digital and real economies can accelerate the transformation of the real economy. In order to explore the multifaceted influence of digital economy on forestry green total factor productivity and its specific presentation form, based on the panel data of 277 cities in China from 2013 to 2019, this paper first used the super SBM model to measure the level of forestry green total factor productivity and adopted the entropy method to measure the level of the digital economy in each region. Secondly, the influence and mechanism of the digital economy on green total factor productivity in forestry were explored by using fixed-effect and intermediate-effect models, and the heterogeneity of the digital economy on forestry green total factor productivity was analyzed based on different regional classification methods. Finally, the spatial spillover effect of the digital economy was explored in depth by the spatial Durbin model. The results are as follows: firstly, there is a significant inverted U-shaped relationship between the digital economy and forestry green total factor productivity, which first promotes and then inhibits. Secondly, the relationship between the digital economy and the level of urban green innovation shows a positive U-shaped relationship, first inhibiting and then promoting, and can have an indirect impact on forestry green total factor productivity by promoting the level of green innovation. Third, China is still on the left side of the inverted U-shaped relationship between the digital economy and forestry green total factor productivity, i.e., it is at a stage where the digital economy can significantly contribute to forestry green total factor productivity. Fourth, the effect of the digital economy on green total factor productivity in forestry is heterogeneous in the east, central, and west and is more pronounced in regions with faster economic development or rich natural resources. Fifth, the impact of the digital economy on forestry green total factor productivity has a significant positive spatial spillover effect. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

24 pages, 8664 KiB  

Open AccessArticle

Seasonal Spatiotemporal Changes in the NDVI and Its Driving Forces in Wuliangsu Lake Basin, Northern China from 1990 to 2020

by Caixia Li, Xiang Jia, Ruoning Zhu, Xiaoli Mei, Dong Wang and Xiaoli Zhang

Remote Sens. 2023, 15(12), 2965; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15122965 - 07 Jun 2023

Cited by 5 | Viewed by 1363

Abstract

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and [...] Read more.

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

18 pages, 1993 KiB  

Open AccessArticle

Assessing Forest Quality through Forest Growth Potential, an Index Based on Improved CatBoost Machine Learning

by Lianjun Cao, Xiaobing He, Sheng Chen and Luming Fang

Sustainability 2023, 15(11), 8888; https://0-doi-org.brum.beds.ac.uk/10.3390/su15118888 - 31 May 2023

Cited by 2 | Viewed by 1827

Abstract

Human activities have always depended on nature, and forests are an important part of this; the determination and improvement of forest quality is therefore highly significant. Currently, domestic and foreign research on forest quality focuses on the current states of forests. We propose [...] Read more.

Human activities have always depended on nature, and forests are an important part of this; the determination and improvement of forest quality is therefore highly significant. Currently, domestic and foreign research on forest quality focuses on the current states of forests. We propose a new research direction based on the future states. By referencing and analyzing the forest quality standards of domestic and foreign experts and institutions, the concept and model for calculating forest growth potential were constructed. Forest growth potential is a new forest quality indicator. Based on the data of 110,000 subcompartments of forest resources from the Lin’an and Landsat8 satellites’ remote sensing data, the unit volume was predicted using three machine-learning algorithms: random gradient descent SGD, the integrated machine learning algorithm CatBoost, and deep learning CNN. The CatBoost algorithm model was improved based on Optuna; then the improved CatBoost algorithm was selected through evaluation indicators for the prediction of forest volume and finally incorporated into the calculation model for forest growth-potential value. The forest growth-potential value was calculated, and an accurate forest quality improvement scheme based on the subcompartments is preliminarily discussed. The successful calculation of forest growth potential values has a certain reference significance, providing guidance for accurately improving forest quality and forest management. The improved CatBoost calculation model is effective in the prediction of forest growth potential, and the determination coefficient R² reaches 0.89, a value that compares favorably with those in other studies. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

22 pages, 4726 KiB  

Open AccessArticle

Spatiotemporal Distribution and Influencing Factors of Coupling Coordination between Digital Village and Green and High-Quality Agricultural Development—Evidence from China

by Heng Wang and Yuting Tang

Sustainability 2023, 15(10), 8079; https://0-doi-org.brum.beds.ac.uk/10.3390/su15108079 - 16 May 2023

Cited by 4 | Viewed by 1518

Abstract

With digital technologies injecting a strong impetus into China’s sustainable agricultural development, the digital village is a new path for Chinese agricultural development. This paper focuses on digital village and sustainable agricultural development, in which the situation of digital village and green and [...] Read more.

With digital technologies injecting a strong impetus into China’s sustainable agricultural development, the digital village is a new path for Chinese agricultural development. This paper focuses on digital village and sustainable agricultural development, in which the situation of digital village and green and high-quality agricultural development in China from 2010 to 2019 is measured based on the entropy weight–TOPSIS method and coupling coordination model and further explores the spatiotemporal evolution of their coupling coordination. In addition, it studies the factors which influence the coupling coordination of digital village and green and high-quality agricultural development using a geographical detector. The study shows that both digital villages and green and high-quality agricultural development in China show good momentum. In terms of the spatial pattern, cities on the southeast coast witness better development of digital villages, and the southern regions enjoy a higher degree of green and high-quality development in agriculture. The coupling coordination between digital villages and green and high-quality agricultural development shows a fluctuating upward trend in the eastern regions. Some of the influence factors play a significant role in the coupling coordination of digital villages and green and high-quality agricultural development, such as e-commerce, per capita income, innovation, and levels of income and education. On this basis, we suggest that the government should continuously promote the development of digital villages and improve rural governance to bridge the digital divide. In this case, policies to promote green and high-quality agricultural development through digitalization can be introduced according to local conditions, thus enabling sustainable agricultural development with the empowerment of digitalization. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-7