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Agronomy
Special Issues
Application of Machine Learning and Modelling in Food Crops
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Application of Machine Learning and Modelling in Food Crops

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1379

Special Issue Editors

Dr. Luís Pádua

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; unmanned aerial vehicles; precision viticulture; precision agriculture; multi-temporal analysis; spectral imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bing Liu

E-Mail Website
Guest Editor
College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: crop modelling; model–data integration; GHG emission; climate impact assessment; climate-smart agriculture; crop phenotyping
Dr. Chenyao Yang

E-Mail Website
Guest Editor Assistant
College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
Interests: crop modelling; agriclimatology; climate impact assessment; climate change adaptation; agro-environmental impacts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing demand for sustainable food production and the challenges posed by climate change, agriculture is undergoing a digital transformation. The integration of machine learning (ML) into agricultural science is revolutionising the way in which we monitor, predict and manage crop productivity, offering data-driven solutions to improve agronomic efficiency, sustainability and resilience.

This Special Issue aims to explore the latest advancements in the applications of ML and computational modelling (including process-based crop modelling) for agricultural crops in relation to remote sensing, Internet of Things (IoT), crop phenotyping, resource optimisation, climate impact assessment and adaptation strategies, pest and disease control, and AI-driven decision support systems, among others. Submissions adopting an innovative interdisciplinary approach across these areas are particularly encouraged, including novel studies that make use of data science to improve monitoring and predictions for crop growth and yield, improve crop management practices, and support crop breeding and policy formulation. Researchers and practitioners are invited to share their results and latest progress in this rapidly evolving field, contributing to the development of intelligent, sustainable and resilient cropping systems.

We look forward to your valuable contributions.

Dr. Luís Pádua
Prof. Dr. Bing Liu
Guest Editors

Dr. Chenyao Yang
Guest Editor Assistant

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

  • crop production and management
  • machine learning
  • deep learning
  • crop modelling
  • Internet of Things (IoT)
  • big data
  • smart agriculture
  • precision farming technologies
  • sensor-based crop monitoring
  • model-based decision support systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 2214 KiB  
Article
Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy
by Chenxiao Li, Jiatong Yu, Sheng Wang, Qinglong Zhao, Qian Song and Yanlei Xu
Agronomy 2025, 15(7), 1505; https://doi.org/10.3390/agronomy15071505 - 21 Jun 2025
Viewed by 297
Abstract
This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and [...] Read more.
This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D1–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D1–RF reached 5.9 for protein, and CARS–SG + D2–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. Full article
(This article belongs to the Special Issue Application of Machine Learning and Modelling in Food Crops)
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19 pages, 4137 KiB  
Article
Evaluation of Suitable Cultivation Regions in China for Siraitia grosvenorii Using a MaxEnt Model and Inductively Coupled Plasma Mass Spectrometry
by Fei Dong, Xiaojie Yan, Jingru Song, Xiyang Huang, Chuanming Fu, Fenglai Lu and Dianpeng Li
Agronomy 2025, 15(6), 1474; https://doi.org/10.3390/agronomy15061474 - 17 Jun 2025
Viewed by 386
Abstract
Global climate change is reshaping the habitat suitability of medicinal plants, potentially compromising their phytochemical integrity and therapeutic efficacy. Siraitia grosvenorii, an edible medicinal plant in China, has expanded its cultivation area into non-native habitats. Therefore, this study analyzed the suitable cultivation [...] Read more.
Global climate change is reshaping the habitat suitability of medicinal plants, potentially compromising their phytochemical integrity and therapeutic efficacy. Siraitia grosvenorii, an edible medicinal plant in China, has expanded its cultivation area into non-native habitats. Therefore, this study analyzed the suitable cultivation region under different periods in China based on the MaxEnt model, and 59 samples were investigated to explore the interrelationships between chemical constituents and climatic variables through multivariate statistical analysis, which will contribute to meeting the sustainable supply of high-quality S. grosvenorii. We discovered that appropriate habitats cover an area of 58.76 × 104 km2, mainly in the southern parts of China. Under future climate conditions, suitable habitats decrease and shift to the northeast along the current habitats. The precipitation levels of the driest month, precipitation seasonality, and temperature seasonality were crucial for its distribution. Furthermore, 11 elements were identified to distinguish samples from different suitable areas through orthogonal partial least squares discriminant analysis. Correlation analysis revealed a strong association between chemical constituents and various climatic factors. This study offers valuable insights into potential S. grosvenorii cultivation areas in China and provides reference indicators for quality evaluation. Full article
(This article belongs to the Special Issue Application of Machine Learning and Modelling in Food Crops)
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31 pages, 7285 KiB  
Article
Development, Design, and Improvement of an Intelligent Harvesting System for Aquatic Vegetable Brasenia schreberi
by Xianping Guan, Longyuan Shi, Hongrui Ge, Yuhan Ding and Shicheng Nie
Agronomy 2025, 15(6), 1451; https://doi.org/10.3390/agronomy15061451 - 14 Jun 2025
Viewed by 340
Abstract
At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts [...] Read more.
At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable Brasenia schreberi was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness. Full article
(This article belongs to the Special Issue Application of Machine Learning and Modelling in Food Crops)
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