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

This special issue belongs to the section “Artificial Intelligence and Digital Agriculture“.

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

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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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Agriculture - ISSN 2077-0472