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Practical Use of Crop, Pest and Diseases Models in Sustainable...
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Practical Use of Crop, Pest and Diseases Models in Sustainable Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2769

Special Issue Editors

Prof. Dr. Chuang Zhao

E-Mail Website
Guest Editor
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100091, China
Interests: crop modelling; climate change; sustainability; food safety
Special Issues, Collections and Topics in MDPI journals
Dr. Liujun Xiao

E-Mail Website
Guest Editor
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu 210095, China
Interests: crop modelling; plant diseases modelling; sustainable agriculture; cropland management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop models serve as critical frameworks for sustainable agriculture, integrating physiological processes, soil–plant interactions, and climatic variables to simulate crop growth, development, and yield under diverse conditions. By quantifying water, nutrient, and light requirements, these models empower farmers to optimize irrigation schedules, fertilizer applications, and crop rotations, thereby enhancing resource efficiency, reducing environmental impacts, and securing yields in the face of climate uncertainty. As global temperatures rise and precipitation patterns shift, models play a pivotal role in assessing risks such as heat-induced stress, drought-driven yield losses, and the spread of crop pests, enabling the design of adaptive strategies like climate-resilient variety selection and precision farming techniques. To optimize crop health and productivity simultaneously, advanced modeling techniques are employed to simulate the dynamics of pests, diseases, and weeds, enabling data-driven decisions for chemical treatments and integrated pest management (IPM) strategies. Advancements in artificial intelligence and machine learning further enhance their capabilities by assimilating real-time data from remote sensors and satellite imagery, refining predictions of growth stages, resource needs, and carbon dynamics. However, challenges persist in harmonizing models across regional scales, and resolving uncertainties in complex agroecosystem interactions, making interdisciplinary collaboration and technological innovation essential to unlocking their full potential.

This Special Issue focuses on innovative crop model applications and the modeling of pests, diseases, and weeds in sustainable agriculture. We welcome research, reviews, and case studies on climate-smart practices, resource efficiency, and resilience, fostering solutions for resilient, sustainable farming systems.

Prof. Dr. Chuang Zhao
Dr. Liujun Xiao
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 250 words) can be sent to the Editorial Office for assessment.

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. Agriculture is an international peer-reviewed open access semimonthly 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 modeling
  • artificial intelligence
  • machine learning
  • modeling of pests, diseases, and weeds
  • climate change

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Published Papers (4 papers)

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Research

20 pages, 1961 KB  
Article
Early Detection of Rice Blast Disease Using Satellite Imagery and Machine Learning on Large Intrafield Datasets
by Alba Agenjos-Moreno, Rubén Simeón, Constanza Rubio, Antonio Uris, Beatriz Ricarte, Belén Franch and Alberto San Bautista
Agriculture 2025, 15(24), 2560; https://doi.org/10.3390/agriculture15242560 - 11 Dec 2025
Viewed by 378
Abstract
This study explores the use of remote sensing and machine learning (ML) for early detection of Pyricularia oryzae (rice blast) in ‘Bomba’ rice. Conducted in Spain’s Albufera Natural Park over four seasons (2021–2024), 94 fields were monitored using Sentinel-2 imagery and Topcon Yield [...] Read more.
This study explores the use of remote sensing and machine learning (ML) for early detection of Pyricularia oryzae (rice blast) in ‘Bomba’ rice. Conducted in Spain’s Albufera Natural Park over four seasons (2021–2024), 94 fields were monitored using Sentinel-2 imagery and Topcon Yield Trakk data. Principal Component Analysis (PCA) identified key spectral bands (B03, B04, B05, B07, B08, B11) at early stages (35 and 55 DAS). Three ML classifiers—K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machines (SVMs)—were tested to categorize fields by yield-based infection levels. RF achieved the best performance (up to 94% Accuracy), showing high robustness across band combinations and dates. KNN was more input-sensitive, and SVM performed weakest. Integrating multispectral and multitemporal data enhanced accuracy. Overall, RF and remote sensing proved reliable tools for early disease detection, supporting Precision Agriculture and real-time pest management. Full article
(This article belongs to the Special Issue Practical Use of Crop, Pest and Diseases Models in Sustainable Agriculture)
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25 pages, 12181 KB  
Article
Characterizing Growth and Estimating Yield in Winter Wheat Breeding Lines and Registered Varieties Using Multi-Temporal UAV Data
by Liwei Liu, Xinxing Zhou, Tao Liu, Dongtao Liu, Jing Liu, Jing Wang, Yuan Yi, Xuecheng Zhu, Na Zhang, Huiyun Zhang, Guohua Feng and Hongbo Ma
Agriculture 2025, 15(24), 2554; https://doi.org/10.3390/agriculture15242554 - 10 Dec 2025
Cited by 1 | Viewed by 449
Abstract
Grain yield is one of the most critical indicators for evaluating the performance of wheat breeding. However, the assessment process, from early-stage breeding lines to officially registered varieties that have passed the DUS (Distinctness, Uniformity, and Stability) test, is often time-consuming and labor-intensive. [...] Read more.
Grain yield is one of the most critical indicators for evaluating the performance of wheat breeding. However, the assessment process, from early-stage breeding lines to officially registered varieties that have passed the DUS (Distinctness, Uniformity, and Stability) test, is often time-consuming and labor-intensive. Multispectral remote sensing based on unmanned aerial vehicles (UAVs) has demonstrated significant potential in crop phenotyping and yield estimation due to its high throughput, non-destructive nature, and ability to rapidly collect large-scale, multi-temporal data. In this study, multi-temporal UAV-based multispectral imagery, RGB images, and canopy height data were collected throughout the entire wheat growth stage (2023–2024) in Xuzhou, Jiangsu Province, China, to characterize the dynamic growth patterns of both breeding lines and registered cultivars. Vegetation indices (VIs), texture parameters (Tes), and a time-series crop height model (CHM), including the logistic-derived growth rate (GR) and the projected area (PA), were extracted to construct a comprehensive multi-source feature set. Four machine learning algorithms, namely a random forest (RF), support vector machine regression (SVR), extreme gradient boosting (XGBoost), and partial least squares regression (PLSR), were employed to model and estimate yield. The results demonstrated that spectral, texture, and canopy height features derived from multi-temporal UAV data effectively captured phenotypic differences among wheat types and contributed to yield estimation. Features obtained from later growth stages generally led to higher estimation accuracy. The integration of vegetation indices and texture features outperformed models using single-feature types. Furthermore, the integration of time-series features and feature selection further improved predictive accuracy, with XGBoost incorporating VIs, Tes, GR, and PA yielding the best performance (R2 = 0.714, RMSE = 0.516 t/ha, rRMSE = 5.96%). Overall, the proposed multi-source modeling framework offers a practical and efficient solution for yield estimation in early-stage wheat breeding and can support breeders and growers by enabling earlier, more accurate selection and management decisions in real-world production environments. Full article
(This article belongs to the Special Issue Practical Use of Crop, Pest and Diseases Models in Sustainable Agriculture)
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16 pages, 1833 KB  
Article
Effects of Water Management Practices on Rice Grain Quality and Pest-Disease Incidence in Environmentally Friendly Cultivation Systems
by SeungKa Oh and Young-Son Cho
Agriculture 2025, 15(21), 2244; https://doi.org/10.3390/agriculture15212244 - 28 Oct 2025
Viewed by 686
Abstract
This study investigated the effects of different water management practices on the growth, yield, and grain quality of rice grown under environmentally friendly farming methods in Apgok-Ri, Gungnyu-Myeon, Uiryeong-Gun, from 2022 to 2024. Treatments included mid-season drainage for 2, 3, or 4 weeks [...] Read more.
This study investigated the effects of different water management practices on the growth, yield, and grain quality of rice grown under environmentally friendly farming methods in Apgok-Ri, Gungnyu-Myeon, Uiryeong-Gun, from 2022 to 2024. Treatments included mid-season drainage for 2, 3, or 4 weeks (2MD, 3MD, 4MD), followed by either low-level water management (MD-1) or alternate wetting and drying (MD-2), with continuous flooding (CF) as the control. The rice variety was machine-transplanted on 9–10 June, and organic fertilizer (90 kg N/ha) was applied as a basal dressing. Water treatments were initiated in mid-July each year. The highest yield was consistently recorded in the 2MD-2 treatment, with 5.85, 5.74, and 5.38 tons/ha from 2022 to 2024, representing 15.0%, 14.5%, and 7.8% increases over CF, respectively. On average, alternate irrigation (MD-2) resulted in higher yields than low-level water management (MD-1) by 1.19–5.90%. Grain quality was also highest in 2MD-2, showing the greatest percentage of ripened grains each year, whereas CF had the highest proportion of immature and unripe grains. Crude protein content in brown rice was lowest in 3MD-2 (6.12%), followed by 2MD-2 (7.51%). Incidences of major diseases such as sheath blight, rice blast, panicle blight, and bacterial grain blight were highest in the CF treatment. Rice leaf blight was not significantly different in 2022, but was most prevalent in CF in 2023 and 2024. There were no major differences in brown planthopper and false smut incidence, although false smut peaked in CF in 2024. These findings suggest that 2-week mid-season drainage followed by alternate irrigation (2MD-2) is an effective strategy to improve yield, grain quality, and disease resistance in sustainable rice farming systems. Full article
(This article belongs to the Special Issue Practical Use of Crop, Pest and Diseases Models in Sustainable Agriculture)
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18 pages, 11608 KB  
Article
YOLO-MSPM: A Precise and Lightweight Cotton Verticillium Wilt Detection Network
by Xinbo Zhao, Jianan Chi, Fei Wang, Xuan Li, Xingcan Yuwen, Tong Li, Yi Shi and Liujun Xiao
Agriculture 2025, 15(19), 2013; https://doi.org/10.3390/agriculture15192013 - 26 Sep 2025
Cited by 1 | Viewed by 653
Abstract
Cotton is one of the world’s most important economic crops, and its yield and quality have a significant impact on the agricultural economy. However, Verticillium wilt of cotton, as a widely spread disease, severely affects the growth and yield of cotton. Due to [...] Read more.
Cotton is one of the world’s most important economic crops, and its yield and quality have a significant impact on the agricultural economy. However, Verticillium wilt of cotton, as a widely spread disease, severely affects the growth and yield of cotton. Due to the typically small and densely distributed characteristics of this disease, its identification poses considerable challenges. In this study, we introduce YOLO-MSPM, a lightweight and accurate detection framework, designed on the YOLOv11 architecture to efficiently identify cotton Verticillium wilt. In order to achieve a lightweight model, MobileNetV4 is introduced into the backbone network. Moreover, a single-head self-attention (SHSA) mechanism is integrated into the C2PSA block, allowing the network to emphasize critical areas of the feature maps and thus enhance its ability to represent features effectively. Furthermore, the PC3k2 module combines pinwheel-shaped convolution (PConv) with C3k2, and the mobile inverted bottleneck convolution (MBConv) module is incorporated into the detection head of YOLOv11. Such adjustments improve multi-scale information integration, enhance small-target recognition, and effectively reduce computation costs. According to the evaluation, YOLO-MSPM achieves precision (0.933), recall (0.920), mAP50 (0.970), and mAP50-95 (0.797), each exceeding the corresponding performance of YOLOv11n. In terms of model lightweighting, the YOLO-MSPM model has 1.773 M parameters, which is a 31.332% reduction compared to YOLOv11n. Its GFLOPs and model size are 5.4 and 4.0 MB, respectively, representing reductions of 14.286% and 27.273%. The study delivers a lightweight yet accurate solution to support the identification and monitoring of cotton Verticillium wilt in environments with limited resources. Full article
(This article belongs to the Special Issue Practical Use of Crop, Pest and Diseases Models in Sustainable Agriculture)
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