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Digital Twins in Precision Agriculture

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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 6847

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Dr. Nathalie dos Santos Guimarães

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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
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Dr. Yuxing Han

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Guest Editor

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
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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 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. Agronomy 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

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

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Published Papers (9 papers)

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Research

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29 pages, 15835 KB

Open AccessArticle

A Lightweight Detection Model for Peanut Leaf Diseases

by Zongle Xiao, Jie Zhou, Xiaoxiao Li, Wei Ma and Fuchun Sun

Agronomy 2026, 16(9), 864; https://doi.org/10.3390/agronomy16090864 - 24 Apr 2026

Viewed by 151

Abstract

Peanut leaf disease detection in complex field environments faces two major challenges: distinguishing visually similar symptoms and identifying severely occluded lesions. This study presents YOLOv8-MSDH, a lightweight detection model built upon an improved YOLOv8 framework to address these issues. Four architectural enhancements are introduced. First, the MHSA attention mechanism is integrated to enhance sequential feature dependency modeling and suppress background noise. Second, the Slim-Neck module is adopted for neck reconstruction, which lowers computational cost and facilitates multi-scale feature fusion. Third, the original C2f module is replaced with the C2f-Dual module to further reduce computational load. Last, the HWD downsampling module is incorporated into the backbone to improve the retention of disease-specific features while promoting lightweight design. Evaluated on a peanut leaf disease dataset, YOLOv8-MSDH achieves 90.14% precision, 82.16% recall, 90.71% mAP50 and 72.14% mAP50-95 under complex conditions—surpassing the baseline YOLOv8 by 3.5, 0.7, 1.5 and 2.89 percentage points, respectively. Parameter count and computational complexity are reduced by 12.9% and 20.7%, confirming effective lightweighting. Operating at 509.95 FPS, the model maintains strong real-time performance and exhibits high robustness across varying lighting conditions. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

30 pages, 5902 KB

Open AccessArticle

Research on a Precision Counting Method and Web Deployment for Natural-Form Bothriochloa ischaemum Spikes and Seeds Based on Object Detection

by Huamin Zhao, Yongzhuo Zhang, Yabo Zheng, Erkang Zeng, Linjun Jiang, Weiqi Yan, Fangshan Xia and Defang Xu

Agronomy 2026, 16(7), 706; https://doi.org/10.3390/agronomy16070706 - 27 Mar 2026

Viewed by 381

Abstract

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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20 pages, 48094 KB

Open AccessArticle

Field-Scale Prediction of Winter Wheat Yield Using Satellite-Derived NDVI

by Edyta Okupska, Antanas Juostas, Dariusz Gozdowski and Elżbieta Wójcik-Gront

Agronomy 2026, 16(6), 670; https://doi.org/10.3390/agronomy16060670 - 22 Mar 2026

Viewed by 474

Abstract

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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19 pages, 9344 KB

Open AccessArticle

UAV Hyperspectral Remote Sensing for Wheat CSPAD Estimation Model Based on Fusion of Spectral Parameters

by Dongwei Han, Weijun Zhang, Muhammad Zain, Jianliang Wang, Shaolong Zhu, Yuanyuan Zhao, Tao Liu, Chengming Sun and Wenshan Guo

Agronomy 2026, 16(4), 430; https://doi.org/10.3390/agronomy16040430 - 11 Feb 2026

Viewed by 509

Abstract

Wheat canopy chlorophyll content (CSPAD) is an important physiological parameter characterizing the photosynthetic capacity and nutritional status of crops. Precision agricultural technologies are widely used for non-destructive monitoring of wheat SPAD, but the SPAD inversion models have limitations due to the incorporation of many principal components besides spectral parameters. In the current study, combined with the SPAD values measured by a handheld instrument, an effective approach for estimating CSPAD from unmanned aerial vehicle (UAV) hyperspectral data is proposed. A fusion modeling scheme based on spectral parameters was constructed by extracting (1) the traditional vegetation index (VI), (2) the sensitive-band index (2D-COSI) screened based on two-dimensional correlation spectroscopy (2D-COS), and (3) the geometric-angle index (SPADSI) constructed by combining the SPA and the PROSAIL model. Finally, the CSPAD estimation model was developed by using Gaussian Process Regression (GPR) and Support Vector Machine Regression (SVM), and their accuracy comparison and feature importance analysis were conducted at different growth stages. We found that the model integrating three types of spectral parameters performed better as compared to the model with a single type of parameter. Further, the GPR model had the highest estimation efficiency at 20 days after the anthesis stage (R² = 0.90, RMSE = 5.95, MAE = 4.47) as compared to the SVM model and other growth stages. This study provides innovative insights and technical support based on a CSPAD estimation framework integrating multiple types of spectral characteristics for the rapid and non-destructive monitoring of wheat CSPAD and for overall sustainability in farmland management. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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34 pages, 15993 KB

Open AccessArticle

A Multispectral UAV Straw Returning Amount Estimation Method Integrating Novel Spectral Calibration and a Deep Learning Model

by Yuanyuan Liu, Xin Tong, Jiaxin Zhang, Xuan Zhao, Junhui Chen, Yuxin Du, Fuxuan Li, Yueyong Wang, Jun Wang, Libin Wang, Meng Yu, Pengxiang Sui and Xiaodan Liu

Agronomy 2026, 16(4), 416; https://doi.org/10.3390/agronomy16040416 - 9 Feb 2026

Viewed by 599

Abstract

Accurately quantifying the amount of corn straw returned to the field is crucial for evaluating conservation tillage measures and phaeozem protection. This study proposes a framework for quantitatively estimating the amount of corn straw returned to the field based on UAV multispectral imaging, integrating a standardized spectral correction strategy, a novel straw index (SI), and an improved deep learning model (convolutional neural network-straw, CNN-Straw). By combining multispectral images acquired by UAVs with ground-measured straw weight data, regression datasets covering autumn and spring conditions were constructed. The proposed straw index aims to enhance the spectral differences between non-photosynthetic straw residues and living vegetation. Furthermore, the CNN-Straw model, combining frequency domain convolution and local spatial attention mechanisms, has an improved ability to represent the complex texture of straw features. Experimental results show that CNN-Straw outperforms traditional machine learning models, including random forest (RF), support vector regression (SVR), and XGBoost, achieving a high coefficient of determination (R²) of 0.82 on different seasonal datasets and effectively reducing the root mean square error (RMSE) and mean absolute error (MAE). Cross-seasonal experiments further demonstrate the stable performance of the framework under different environmental conditions. The proposed method provides an efficient and scalable solution for the quantitative assessment of straw return to the field, supporting precision agricultural management and phaeozem conservation practices. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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21 pages, 16271 KB

Open AccessEditor’s ChoiceArticle

Soybean Leaf Disease Recognition Methods Based on Hyperparameter Transfer and Progressive Fine-Tuning of Large Models

by Xiaoming Li, Wenxue Bian, Boyu Yang, Yongguang Li, Shiqi Wang, Ning Qin, Shanglong Ye, Zunyang Bao and Hongmin Sun

Agronomy 2026, 16(2), 218; https://doi.org/10.3390/agronomy16020218 - 16 Jan 2026

Viewed by 600

Abstract

Early recognition of crop diseases is essential for ensuring agricultural security and improving yield. However, traditional CNN-based methods often suffer from limited generalization when training data are scarce or when applied to transfer scenarios. To address these challenges, this study adopts the multimodal large model Qwen2.5-VL as the core and targets three major soybean leaf diseases along with healthy samples. We propose a parameter-efficient adaptation framework that integrates cross-architecture hyperparameter transfer and progressive fine-tuning. The framework utilizes a Vision Transformer (ViT) as an auxiliary model, where Bayesian optimization is applied to obtain optimal hyperparameters that are subsequently transferred to Qwen2.5-VL. Combined with existing low-rank adaptation (LoRA) and a multi-stage training strategy, the framework achieves efficient convergence and robust generalization with limited data. To systematically evaluate the model’s multi-scale visual adaptability, experiments were conducted using low-resolution, medium-resolution, and high-resolution inputs. The results demonstrate that Qwen2.5-VL achieves an average zero-shot accuracy of 71.72%. With the proposed cross-architecture hyperparameter transfer and parameter-efficient tuning strategy, accuracy improves to 88.72%, and further increases to 93.82% when progressive fine-tuning is applied. The model also maintains an accuracy of 91.0% under cross-resolution evaluation. Overall, the proposed method exhibits strong performance in recognition accuracy, feature discriminability, and multi-scale robustness, providing an effective reference for adapting multimodal large language models to plant disease identification tasks. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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20 pages, 7370 KB

Open AccessArticle

Hierarchical Deep Learning Framework for Mapping Honey-Producing Tree Species in Dense Forest Ecosystems Using Sentinel-2 Imagery

by Athanasios Antonopoulos, Tilemachos Moumouris, Vasileios Tsironis, Athena Psalta, Evangelia Arapostathi, Antonios Tsagkarakis, Panayiotis Trigas, Paschalis Harizanis and Konstantinos Karantzalos

Agronomy 2025, 15(12), 2858; https://doi.org/10.3390/agronomy15122858 - 12 Dec 2025

Viewed by 675

Abstract

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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18 pages, 5597 KB

Open AccessArticle

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 - 13 Oct 2025

Viewed by 980

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 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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Review

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24 pages, 5577 KB

Open AccessReview

Intelligent Batch Harvesting of Trellis-Grown Fruits with Application to Kiwifruit Picking Robots

by Yuxin Yang, Mei Zhang, Wei Ma and Yongsong Hu

Agronomy 2025, 15(11), 2499; https://doi.org/10.3390/agronomy15112499 - 28 Oct 2025

Cited by 1 | Viewed by 1673

Abstract

This study aims to help researchers quickly understand the latest research status of kiwifruit picking robots to expand their research ideas. The centralized picking of kiwifruit is confronted with challenges such as high labor intensity and labor shortage. A series of social issues including the decline in agricultural population and population aging have further increased the cost of its harvest. Therefore, intelligent picking robots replacing manual operations is an effective solution. This paper, through literature review and organization, analyzes and evaluates the performance characteristics of various current kiwifruit picking robots. It summarizes the key technologies of kiwifruit picking robots, from the aspects of robot vision systems, mechanical arms, and the end effector. At the same time, it conducts an in-depth analysis of the problems existing in automatic kiwifruit harvesting technology in modern agriculture. Finally, it is concluded that in the future, research should be carried out in aspects such as kiwifruit cluster recognition algorithms, picking efficiency, and damage cost and universality to enhance the operational performance and market promotion potential of kiwifruit picking robots. The significance of this review lies in addressing the imminent labor crisis in agricultural production and steering agriculture toward intelligent and precise transformation. Its contributions are reflected in greatly advancing robotic technology in complex agricultural settings, generating substantial technical achievements, injecting new vitality into related industries and academic fields, and ultimately delivering sustainable economic benefits and stable agricultural supply to society. Full article

(This article belongs to the Special Issue Digital Twins in Precision Agriculture)

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