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Agriculture
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Remote and Proximal Sensing for Arable Crops Monitoring and Yield...
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Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1415

Special Issue Editors

Dr. Emmanouil Psomiadis

E-Mail Website
Guest Editor
Natural Resources and Agricultural Engineering, Agricultural University of Athens, Votanikos, 11855 Athens, Greece
Interests: remote sensing; UAV; satellite data; precision agriculture; GIS; geospatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicholas Dercas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, Athens, Greece
Interests: irrigation; hydrology; water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote and proximal sensing technologies have rapidly transformed arable crop monitoring over the last two decades, evolving from simple spectral index analysis to sophisticated multi-sensor, multi-scale, and AI-driven approaches. Recent advancements in satellite missions, UAV multispectral/thermal imaging, LiDAR, proximal canopy sensors, and soil–plant–atmosphere monitoring systems have enabled unprecedented spatial and temporal detail in assessing crop conditions, stress responses, and yield formation processes.

This Special Issue aims to bring together cutting-edge research that leverages Earth observation, field-based sensing, artificial intelligence, and data-fusion methodologies to improve crop monitoring, input optimization, and yield assessment in arable systems. We invite contributions showcasing methodological innovations, operational applications, and integrative frameworks that support sustainable crop production and precision agriculture.

We particularly welcome studies that combine multi-scale datasets, exploit machine learning or deep learning models, develop decision-support tools, or demonstrate real-world implementations.

We seek original research articles, technical developments, reviews, and case studies covering remote sensing, proximal sensing, AI analytics, data fusion, variable-rate applications, and yield prediction for major arable crops (e.g., cereals, cotton, maize, legumes). Submissions addressing climate-smart agriculture, resource-efficient management, and regional upscaling of field-level monitoring approaches are especially encouraged.

Dr. Emmanouil Psomiadis
Prof. Dr. Nicholas Dercas
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

  • remote sensing
  • proximal sensing
  • UAV multispectral/thermal imaging
  • LiDAR
  • vegetation indices
  • crop monitoring
  • yield assessment
  • data fusion
  • artificial intelligence
  • machine learning
  • precision agriculture
  • digital agriculture

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Published Papers (3 papers)

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Research

26 pages, 3635 KB  
Article
Bayesian Additive Regression Trees for Multi-Depth Soil Moisture Modeling
by Dimitrios Koulouris and Nikolaos Malamos
Agriculture 2026, 16(10), 1120; https://doi.org/10.3390/agriculture16101120 - 21 May 2026
Viewed by 226
Abstract
Soil moisture content (SMC) is a key variable in hydrology, irrigation, and land-atmosphere interactions, yet continuous monitoring remains constrained by sensor limitations and site heterogeneity. This study evaluated Bayesian Additive Regression Trees (BART) for estimating daily SMC at 10, 30, and 50 cm [...] Read more.
Soil moisture content (SMC) is a key variable in hydrology, irrigation, and land-atmosphere interactions, yet continuous monitoring remains constrained by sensor limitations and site heterogeneity. This study evaluated Bayesian Additive Regression Trees (BART) for estimating daily SMC at 10, 30, and 50 cm depths in the Arta plain, northwestern Greece, using combinations of in situ soil moisture observations from other depths together with Sentinel-2-derived NDVI and NDMI. BART was trained with 2020–2021 data and evaluated using 2022 observations. Model performance was generally high, with Nash–Sutcliffe efficiency often exceeding 0.90 and RMSE remaining below nominal sensor uncertainty. The best results were obtained when soil moisture from two additional depths was used as predictor information, confirming the strong vertical dependence of profile moisture dynamics. NDVI and NDMI did not systematically improve point prediction accuracy but provided complementary information by improving the estimation of predictive uncertainty and generating more reliable credible intervals within the probabilistic formulation. Residuals were normally distributed and showed no evident systematic bias. Preliminary external validation at an independent site showed moderate skill, with most cases still producing errors below nominal sensor accuracy. Finally, a comparison between BART and Multiple Linear Regression (MLR) showed that BART outperformed MLR, particularly in cases where both machine learning models performed weakly. Overall, BART proved to be a robust framework for multi-depth soil moisture estimation. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment)
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32 pages, 30028 KB  
Article
A Multi-Class Crop Field Identification Method Based on Semantic–SAM Fusion and UAV RGB Imagery
by Haoran Yang, Xinjun Wang, Qingfu Liang, Shuhan Huang, Panfeng Wang and Jiandong Sheng
Agriculture 2026, 16(10), 1108; https://doi.org/10.3390/agriculture16101108 - 18 May 2026
Viewed by 284
Abstract
Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based [...] Read more.
Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based semantic segmentation models can effectively identify crop types, they often produce coarse or incomplete boundaries. The Segment Anything Model (SAM) can produce high-quality boundaries, but it depends on manual prompts and lacks semantic recognition ability, which limits its use in large-scale automatic mapping. To address this issue, this study proposes a parcel-level crop field identification framework based on Semantic–SAM fusion, enabling automatic semantic recognition and fine boundary extraction without manual prompts. Based on UAV RGB remote sensing imagery, this study developed a two-stage Semantic–SAM framework. Semantic segmentation models, including DeepLabv3+, U-Net, HRNet, and PSPNet, were first used to generate initial results. Then, bounding boxes or internal high-confidence points were extracted from the initial field regions as prompts for SAM to refine the segmentation. The final results preserved crop category information while producing finer boundaries. To evaluate the framework, this study compared four semantic segmentation models and their Semantic–SAM versions on the same-region test set, and further tested their spatial generalization ability on the different-region test set. The results showed that the Semantic–SAM framework provided more consistent gains in boundary quality, with regional recognition accuracy improving in several models and test scenarios. On the same-region test set, the PSPNet-based framework showed clear improvement, with mean Intersection over Union (mIoU) increasing from 78.99% to 83.13% under point-box prompts. The U-Net-based framework achieved the best mIoU of 87.09% with box prompts. On the different-region test set, the DeepLabv3+-based framework showed the largest gain in spatial generalization, with mIoU increasing from 67.22% to 73.45% under point-box prompts. Overall, the PSPNet-based fusion framework showed a better balance in accuracy, boundary quality, and robustness under different-region conditions. These results demonstrate that Semantic–SAM fusion supports automatic multi-class crop field mapping and boundary refinement from UAV RGB imagery without manual prompts or SAM fine-tuning, providing a practical approach for parcel-level crop monitoring and precision agriculture applications. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment)
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23 pages, 48382 KB  
Article
Spatial Distribution and System Constraints Diagnosis of Medium- and Low-Yield Farmlands in Northern China Based on Remote Sensing
by Xiangyang Sun, Zhenlin Tian, Zhanqing Zhao, Yuping Lei, Wenxu Dong, Chunsheng Hu, Chaobo Zhang and Xiuping Liu
Agriculture 2026, 16(8), 896; https://doi.org/10.3390/agriculture16080896 - 17 Apr 2026
Viewed by 378
Abstract
Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale [...] Read more.
Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment)
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