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Agronomy
Special Issues
Digital Twins in Precision Agriculture
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Digital Twins in Precision Agriculture

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

Deadline for manuscript submissions: 20 May 2026 | Viewed by 130

Special Issue Editors

Dr. Nathalie Guimarães

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: smart farming; crop monitoring; precision agriculture; remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxing Han

E-Mail Website
Guest Editor
1. College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518000, China
Interests: computer vision; deep learning; brain-inspired computing; edge computing; remote sensing; agricultural engineering; smart agriculture; precision agriculture; agricultural aviation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital twin technology is being increasingly applied in agriculture. This progress is driven by the integration of real-time data from satellite remote sensing, UAVs, IoT field sensors, and machine learning algorithms to create dynamic, data-driven virtual models of agricultural systems. Building on its origins in industry and systems engineering, this paradigm enables continuous monitoring, scenario simulation, and decision support for complex crop environments.

This Special Issue focuses on the integration of AI-driven modeling, multispectral and hyperspectral imaging, data fusion frameworks, and physics-informed neural networks to develop robust digital twins for precision agriculture. Core topics include crop growth simulation, stress detection, yield forecasting, and feedback-based control systems.

We invite contributions exploring novel architectures, real-world deployments, sensor-to-model pipelines, and interoperable platforms that connect Earth observation data with predictive analytics. Both methodological innovations and application-focused case studies are welcome, especially those addressing climate resilience, explainability in AI, and scalable digital infrastructure for smart farming systems.

Dr. Nathalie Guimarães
Dr. Yuxing Han
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

  • digital twins
  • precision agriculture
  • remote sensing
  • machine learning
  • deep learning
  • crop simulation
  • smart farming

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Published Papers (1 paper)

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Research

20 pages, 4630 KB  
Article
Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data
by Yuanyuan Zhao, Hui Wang, Wei Wu, Yi Sun, Ying Wang, Weijun Zhang, Jianliang Wang, Fei Wu, Wouter H. Maes, Jinfeng Ding, Chunyan Li, Chengming Sun, Tao Liu and Wenshan Guo
Agronomy 2025, 15(10), 2384; https://doi.org/10.3390/agronomy15102384 (registering DOI) - 13 Oct 2025
Abstract
In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This [...] Read more.
In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding. Full article
(This article belongs to the Special Issue Digital Twins in Precision Agriculture)
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