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Agronomy
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Food and Agricultural Imaging Systems – An Outlook to the...
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Food and Agricultural Imaging Systems – An Outlook to the Future

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 11333

Special Issue Editor

Dr. Chuanqi Xie

E-Mail Website
Guest Editor
1. The Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
2. State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: agricultural engineering; animal husbandry engineering; modeling; phenotyping; machine learning

Special Issue Information

Dear Colleagues,

Traditional analytical and detection techniques in agriculture tend to be expensive, labor-intensive, inefficient and time-consuming. With the development of remote sensing and machine learning, modern agriculture requires more advanced techniques. In terms of specificity, sensitivity, simplicity, low cost, rapidity and non-destructivity, optical imaging techniques are effective tools for the evaluation and inspection of food and agriculture.

Thus, the aim and scope of this Special Issue focuses on imaging systems (RGB images, multispectral images, hyperspectral images, and depth images, etc.), machine learning, remote sensing and big data analysis in food and agriculture. The topics of this Special Issue include image processing, data analysis, machine learning and modeling in food and agriculture (crops and animals) based on both laboratory platforms and field scales (ground vehicles, and unmanned aerial vehicles, etc.). Both research articles and reviews are welcome.

Dr. Chuanqi Xie
Guest Editor

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

  • agricultural engineering
  • animal husbandry engineering
  • food safety and nutrition
  • imaging systems
  • machine learning
  • Internet of Things
  • image processing
  • big data analysis
  • remote sensing
  • sensors

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Published Papers (5 papers)

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Research

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21 pages, 10537 KiB  
Article
Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain
by Xiaoyong Zhang, Yonglin Guo, Xiangyu Tian and Yongqing Bai
Agronomy 2023, 13(11), 2800; https://doi.org/10.3390/agronomy13112800 - 12 Nov 2023
Viewed by 1821
Abstract
Northern Slopes of Tianshan Mountain (NSTM) in Xinjiang hold significance as a principal agricultural hub within the region’s arid zone. Accurate crop mapping across vast agricultural expanses is fundamental for intelligent crop monitoring and devising sustainable agricultural strategies. Previous studies on multi-temporal crop [...] Read more.
Northern Slopes of Tianshan Mountain (NSTM) in Xinjiang hold significance as a principal agricultural hub within the region’s arid zone. Accurate crop mapping across vast agricultural expanses is fundamental for intelligent crop monitoring and devising sustainable agricultural strategies. Previous studies on multi-temporal crop classification have predominantly focused on single-point pixel temporal features, often neglecting spatial data. In large-scale crop classification tasks, by using spatial information around the pixel, the contextual relationships of the crop can be obtained to reduce possible noise interference. This research introduces a multi-scale, multi-temporal classification framework centered on ConvGRU (convolutional gated recurrent unit). By leveraging the attention mechanism of the Strip Pooling Module (SPM), a multi-scale spatial feature extraction module has been designed. This module accentuates vital spatial and spectral features, enhancing the clarity of crop edges and reducing misclassifications. The temporal information fusion module integration features from various periods to bolster classification precision. Using Sentinel-2 imagery spanning May to October 2022, datasets for cotton, corn, and winter wheat of the NSTM were generated for the framework’s training and validation. The results demonstrate an impressive 93.03% accuracy for 10 m resolution crop mapping using 15-day interval, 12-band Sentinel-2 data for the three crops. This method outperforms other mainstream methods like Random Forest (RF), Long Short-Term Memory (LSTM), Transformer, and Temporal Convolutional Neural Network (TempCNN), showcasing a kappa coefficient of 0.9062, 7.52% and 2.42% improvement in Overall Accuracy compared to RF and LSTM, respectively, which demonstrate the potential of our model for large-scale crop classification tasks to enable high-resolution crop mapping on the NSTM. Full article
(This article belongs to the Special Issue Food and Agricultural Imaging Systems – An Outlook to the Future)
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24 pages, 10876 KiB  
Article
The Detection of Kiwifruit Sunscald Using Spectral Reflectance Data Combined with Machine Learning and CNNs
by Ke Wu, Zhicheng Jia and Qifeng Duan
Agronomy 2023, 13(8), 2137; https://doi.org/10.3390/agronomy13082137 - 15 Aug 2023
Cited by 6 | Viewed by 2006
Abstract
Sunscald in kiwifruit, an environmental stress caused by solar radiation during the summer, reduces fruit quality and yields and causes economic losses. The efficient and timely detection of sunscald and similar diseases is a challenging task but helps to implement measures to control [...] Read more.
Sunscald in kiwifruit, an environmental stress caused by solar radiation during the summer, reduces fruit quality and yields and causes economic losses. The efficient and timely detection of sunscald and similar diseases is a challenging task but helps to implement measures to control stress. This study provides high-precision detection models and relevant spectral information on kiwifruit physiology for similar statuses, including early-stage sunscald, late-stage sunscald, anthracnose, and healthy. Primarily, in the laboratory, 429 groups of spectral reflectance data for leaves of four statuses were collected and analyzed using a hyperspectral reflection acquisition system. Then, multiple modeling approaches, including combined preprocessing methods, feature extraction algorithms, and classification algorithms, were designed to extract bands and evaluate the performance of the models to detect the statuses of kiwifruit. Finally, the detection of different stages of kiwifruit sunscald under anthracnose interference was accomplished. As influential bands, 694–713 nm, 758–777 nm, 780–799 nm, and 1303–1322 nm were extracted. The overall accuracy, precision, recall, and F1-score values of the models reached 100%, demonstrating an ability to detect all statuses with 100% accuracy. It was concluded that the combined processing of moving average and standard normal variable transformations (MS) could significantly improve the data; the near-infrared support vector machine and visible convolutional neural network with MS (NIR-MS-SVM and VIS-MS-CNN) were established as high-precision detection techniques for the classification of similar kiwifruit statuses, demonstrating 25.58% higher accuracy than the single support vector machine. The VIS-MS-CNN model reached convergence with a stable cross-entropy loss of 0.75 in training and 0.77 in validation. The techniques developed in this study will improve orchard management efficiency and yields and increase researchers’ understanding of kiwifruit physiology. Full article
(This article belongs to the Special Issue Food and Agricultural Imaging Systems – An Outlook to the Future)
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15 pages, 3258 KiB  
Article
Morley: Image Analysis and Evaluation of Statistically Significant Differences in Geometric Sizes of Crop Seedlings in Response to Biotic Stimulation
by Daria D. Emekeeva, Tomiris T. Kusainova, Lev I. Levitsky, Elizaveta M. Kazakova, Mark V. Ivanov, Irina P. Olkhovskaya, Mikhail L. Kuskov, Alexey N. Zhigach, Nataliya N. Glushchenko, Olga A. Bogoslovskaya and Irina A. Tarasova
Agronomy 2023, 13(8), 2134; https://doi.org/10.3390/agronomy13082134 - 15 Aug 2023
Viewed by 1919
Abstract
Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and [...] Read more.
Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots, and roots in seedling images; (3) calculation of shoot and root lengths and surface area; (4) evaluation of statistically significant differences between plant groups; (5) calculation of germination rates; and (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when the molecular mechanisms underlying the morphometric changes are analyzed. The performance was tested using cultivars of Triticum aestivum and Pisum sativum on seedlings of up to 1 week old. The accuracy of the measured morphometric parameters was comparable with that obtained using ImageJ and manual measurements. Possible applications of Morley include dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers. Full article
(This article belongs to the Special Issue Food and Agricultural Imaging Systems – An Outlook to the Future)
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12 pages, 3320 KiB  
Article
Fast Determination of Amylose Content in Lotus Seeds Based on Hyperspectral Imaging
by Xuan Wei, Liang Huang, Siyi Li, Sheng Gao, Dengfei Jie, Zebin Guo and Baodong Zheng
Agronomy 2023, 13(8), 2104; https://doi.org/10.3390/agronomy13082104 - 10 Aug 2023
Cited by 3 | Viewed by 1517
Abstract
Different varieties of fresh lotus seeds have varying levels of amylose content. It has a direct impact on the following processing and final product quality, so the non-destructive detection of amylose content is meaningful before lotus seed production. This study proposed a non-destructive [...] Read more.
Different varieties of fresh lotus seeds have varying levels of amylose content. It has a direct impact on the following processing and final product quality, so the non-destructive detection of amylose content is meaningful before lotus seed production. This study proposed a non-destructive method to detect the amylose content of fresh lotus seeds. Hyperspectral images of 120 fresh lotus seeds of three different varieties were obtained, and different pretreatments were applied to the average spectra obtained from the region of interest (ROI). The calibration and prediction set were divided by the sample set joint x–y distances algorithm (SPXY). Then, the partial lease square regression (PLSR) method was established for modeling, with Savitzky–Golay pretreatment-based PLSR showing the best results. To further improve the stability of the predictive model, different methods of feature variables selection were compared. The results showed that the best PLSR model was established with the inputs of 15 feature bands selected from 472 bands by the successive projection algorithm (SPA). The correlation coefficient of the prediction set (Rp), root mean square error of the prediction set (RMSEP), and residual predictive deviation (RPD) were 0.890, 15.154 mg g−1, and 2.193, respectively. Meanwhile, this study visualized the amylose content distribution maps from which it could estimate the content level directly. This study could provide a reference for further development of portable detection equipment for the amylose content of fresh lotus seeds. Full article
(This article belongs to the Special Issue Food and Agricultural Imaging Systems – An Outlook to the Future)
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Review

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29 pages, 3750 KiB  
Review
An Overview of Recent Advances in Greenhouse Strawberry Cultivation Using Deep Learning Techniques: A Review for Strawberry Practitioners
by Jong-Won Yang and Hyun-Il Kim
Agronomy 2024, 14(1), 34; https://doi.org/10.3390/agronomy14010034 - 21 Dec 2023
Cited by 7 | Viewed by 3203
Abstract
Strawberry (Fragaria × ananassa Duch.) has been widely accepted as the “Queen of Fruits”. It has been identified as having high levels of vitamin C and antioxidants that are beneficial for maintaining cardiovascular health and maintaining blood sugar levels. The implementation [...] Read more.
Strawberry (Fragaria × ananassa Duch.) has been widely accepted as the “Queen of Fruits”. It has been identified as having high levels of vitamin C and antioxidants that are beneficial for maintaining cardiovascular health and maintaining blood sugar levels. The implementation of advanced techniques like precision agriculture (PA) is crucial for enhancing production compared to conventional farming methods. In recent years, the successful application of deep learning models was represented by convolutional neural networks (CNNs) in a variety of disciplines of computer vision (CV). Due to the dearth of a comprehensive and detailed discussion on the application of deep learning to strawberry cultivation, a particular review of recent technologies is needed. This paper provides an overview of recent advancements in strawberry cultivation utilizing Deep Learning (DL) techniques. It provides a comprehensive understanding of the most up-to-date techniques and methodologies used in this field by examining recent research. It also discusses the recent advanced variants of the DL model, along with a fundamental overview of CNN architecture. In addition, techniques for fine-tuning DL models have been covered. Besides, various strawberry-planting-related datasets were examined in the literature, and the limitations of using research models for real-time research have been discussed. Full article
(This article belongs to the Special Issue Food and Agricultural Imaging Systems – An Outlook to the Future)
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