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Agronomy
Special Issues
Monitoring and Forecasting Techniques in Fruit and Vegetable Production
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Monitoring and Forecasting Techniques in Fruit and Vegetable Production

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 10738

Special Issue Editors

Prof. Dr. Jingcheng Zhang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China
Interests: agricultural remote sensing; algorithms and models for processing multi-source geological data
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Yang

E-Mail Website
Guest Editor
1. National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 10089, China
2. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: orchard monitoring; crop phenotyping; LiDAR; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fruits and vegetables are important agricultural products. With the continuous strengthening of people's awareness of health and hygienic diets, the consumption of fruits and vegetables is increasing worldwide, which has put forward higher requirements on the yield and quality of fruits and vegetables. To facilitate efficient management and lowering the risk in the production process, some monitoring and forecasting techniques play a key role in understanding the growing status and potential risks.

The evolution of remote sensing technology, wireless sensor networks, deep learning, machine learning and forecasting models and theories has opened up new prospects for supporting the production and management of fruits and vegetables. To optimise agronomic inputs and resilience, reduce the impact from stresses and disasters and improve yield/quality and production efficiency, this Special Issue aims at highlighting state-of-the-art monitoring and forecasting techniques in fruit and vegetable production. This Special Issue invites contributions on: (i) innovative monitoring techniques in fruit and vegetable production; (ii) novel forecasting modelling methodologies on yield, quality and disasters; and (iii) literature reviews or applications of monitoring and forecasting techniques. Submissions are expected to cover a broad range of topics which may include, but are not limited to, the following:

  • Monitoring of growing status with remote sensing and WSNs;
  • Monitoring of stresses in fruit and vegetable production;
  • Open area (e.g., orchards) and indoor (e.g., green house) monitoring techniques;
  • Monitoring techniques associated with spectral and imaging analysis;
  • Fusion of multiple sources of data in monitoring and forecasting;
  • Forecasting models of yield, quality, diseases and pests and meteorological disasters;
  • Theories and models for forecasting tasks;
  • Novel data processing techniques in forecasting.

Prof. Dr. Jingcheng Zhang
Prof. Dr. Hao Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • growth
  • yield
  • quality
  • diseases and pests
  • meteorological disaster
  • forecasting
  • monitoring
  • modeling

Published Papers (4 papers)

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Research

16 pages, 5143 KiB  
Article
Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning
by Jinpeng Cheng, Guijun Yang, Weimeng Xu, Haikuan Feng, Shaoyu Han, Miao Liu, Fa Zhao, Yaohui Zhu, Yu Zhao, Baoguo Wu and Hao Yang
Agronomy 2022, 12(7), 1497; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12071497 - 22 Jun 2022
Cited by 7 | Viewed by 1839
Abstract
As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative [...] Read more.
As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC. Full article
(This article belongs to the Special Issue Monitoring and Forecasting Techniques in Fruit and Vegetable Production)
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15 pages, 2661 KiB  
Article
Explainable Deep Learning Study for Leaf Disease Classification
by Kaihua Wei, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu and Dongmei Chen
Agronomy 2022, 12(5), 1035; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12051035 - 26 Apr 2022
Cited by 16 | Viewed by 3152
Abstract
Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied. We studied the interpretability of deep learning models in different agricultural classification tasks based on the fruit leaves dataset. The [...] Read more.
Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied. We studied the interpretability of deep learning models in different agricultural classification tasks based on the fruit leaves dataset. The purpose is to explore whether the classification model is more inclined to extract the appearance characteristics of leaves or the texture characteristics of leaf lesions during the feature extraction process. The dataset was arranged into three experiments with different categories. In each experiment, the VGG, GoogLeNet, and ResNet models were used and the ResNet-attention model was applied with three interpretable methods. The results show that the ResNet model has the highest accuracy rate in the three experiments, which are 99.11%, 99.4%, and 99.89%, respectively. It is also found that the attention module could improve the feature extraction of the model, and clarify the focus of the model in different experiments when extracting features. These results will help agricultural practitioners better apply deep learning models to solve more practical problems. Full article
(This article belongs to the Special Issue Monitoring and Forecasting Techniques in Fruit and Vegetable Production)
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15 pages, 4993 KiB  
Article
Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data
by Yujuan Huang, Jingcheng Zhang, Jingwen Zhang, Lin Yuan, Xianfeng Zhou, Xingang Xu and Guijun Yang
Agronomy 2022, 12(3), 679; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12030679 - 11 Mar 2022
Cited by 8 | Viewed by 2256
Abstract
Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies [...] Read more.
Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices. Full article
(This article belongs to the Special Issue Monitoring and Forecasting Techniques in Fruit and Vegetable Production)
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17 pages, 7066 KiB  
Article
Culling Double Counting in Sequence Images for Fruit Yield Estimation
by Xue Xia, Xiujuan Chai, Ning Zhang, Zhao Zhang, Qixin Sun and Tan Sun
Agronomy 2022, 12(2), 440; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12020440 - 10 Feb 2022
Cited by 5 | Viewed by 2390
Abstract
Exact yield estimation of fruits on plants guaranteed fine and timely decisions on harvesting and marketing practices. Automatic yield estimation based on unmanned agriculture offers a viable solution for large orchards. Recent years have witnessed notable progress in computer vision with deep learning [...] Read more.
Exact yield estimation of fruits on plants guaranteed fine and timely decisions on harvesting and marketing practices. Automatic yield estimation based on unmanned agriculture offers a viable solution for large orchards. Recent years have witnessed notable progress in computer vision with deep learning for yield estimation. Yet, the current practice of vision-based yield estimation with successive frames may engender fairly great error because of the double counting of repeat fruits in different images. The goal of this study is to provide a wise framework for fruit yield estimation in sequence images. Specifically, the anchor-free detection architecture (CenterNet) is utilized to detect fruits in sequence images from videos collected in the apple orchard and orange orchard. In order to avoid double counts of a single fruit between different images in an image sequence, the patch matching model is designed with the Kuhn–Munkres algorithm to optimize the paring process of repeat fruits in a one-to-one assignment manner for the sound performance of fruit yield estimation. Experimental results show that the CenterNet model can successfully detect fruits, including apples and oranges, in sequence images and achieved a mean Average Precision (mAP) of 0.939 under an IoU of 0.5. The designed patch matching model obtained an F1-Score of 0.816 and 0.864 for both apples and oranges with good accuracy, precision, and recall, which outperforms the performance of the reference method. The proposed pipeline for the fruit yield estimation in the test image sequences agreed well with the ground truth, resulting in a squared correlation coefficient of R2apple = 0.9737 and R2orange = 0.9562, with a low Root Mean Square Error (RMSE) for these two varieties of fruit. Full article
(This article belongs to the Special Issue Monitoring and Forecasting Techniques in Fruit and Vegetable Production)
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