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Agronomy
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Precision Monitoring of Crops and Pastures Using UAV, Satellite, and...
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Precision Monitoring of Crops and Pastures Using UAV, Satellite, and Sensor Technologies

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 4101

Special Issue Editors

Dr. Jitka Kumhálová

E-Mail Website
Guest Editor
Department of Vehicles and Ground Transport, Czech University of Life Sciences Prague, Prague, Czech Republic
Interests: GIS; remote sensing; UAV; satellite images; photogrammetry; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Iñigo Molina

E-Mail Website
Guest Editor
Department of Topographic and Cartographic Engineering, Universidad Politécnica de Madrid, Madrid, Spain
Interests: soil moisture content (SMC); global navigation satellite systems reflectometry (GNSS-R); active-passive sensors; earth-science applications
Special Issues, Collections and Topics in MDPI journals
Dr. František Kumhála

E-Mail Website
Guest Editor
Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic
Interests: sensors for agriculture; precision farming; yield sensors; moisture sensors

Special Issue Information

Dear Colleagues,

With the development of crop production monitoring using smart and digital tools (GIS and remote sensing), the number of those using these advanced tools is also increasing. In recent years, there has been more focus on the use of artificial intelligence and machine learning, which enable automation processes within the concept of smart agriculture. Developments in this area have also followed recent technological advances in the field of sensors and platforms (the development of modern UAV concepts, precise sensors, etc.). This Special Issue will focus on recent advances in the application of geoinformatics in agriculture for the precise monitoring of crops and pastures using UAVs, satellites, and sensor technologies. Research articles, communications, and review articles are welcome. We particularly support contributions related to geoinformatics methods implemented in agricultural practice. Methods of interest include the use and application of GIS, especially its advanced tools and solutions. Special attention will also be paid to research involving the implementation of remote sensing methods, such as optical and microwave remote sensing and its application in crop and livestock production. Studies that focus on the development of the use of unmanned aerial vehicles in an agricultural context and the development of new solutions based on photogrammetry methods for monitoring crop growth, including that of special crops, are particularly encouraged.

Dr. Jitka Kumhálová
Prof. Dr. Iñigo Molina
Dr. František Kumhála
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

  • active and passive remote sensing
  • UAV
  • GIS
  • Lidar and image photogrammetry
  • crop production
  • pastures
  • special crops

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 9456 KB  
Article
A Multi-Feature Estimation Model for Olive Canopy Chlorophyll Combining XGBoost with UAV Imagery
by Weiyu Zhuang, Dong Li, Weili Kou, Ning Lu, Fan Wu, Shixian Sun and Zhefeng Liu
Agronomy 2025, 15(12), 2718; https://doi.org/10.3390/agronomy15122718 - 26 Nov 2025
Cited by 4 | Viewed by 1034
Abstract
Olive (Olea europaea L.) is an important woody oil crop worldwide, and accurate estimation of leaf chlorophyll content is critical for assessing nutritional status, photosynthetic capacity, and precision crop management. Unmanned aerial vehicle (UAV) remote sensing, with high spatiotemporal resolution, has increasingly [...] Read more.
Olive (Olea europaea L.) is an important woody oil crop worldwide, and accurate estimation of leaf chlorophyll content is critical for assessing nutritional status, photosynthetic capacity, and precision crop management. Unmanned aerial vehicle (UAV) remote sensing, with high spatiotemporal resolution, has increasingly been applied in crop growth monitoring. However, the small, thick, waxy leaves of olive, together with its complex canopy structure and dense arrangement, may reduce estimation accuracy. To identify sensitive features related to olive leaf chlorophyll and to evaluate the feasibility of UAV-based estimation methods for olive trees with complex canopy structures, UAV multispectral orthophotos were acquired, and leaf chlorophyll was measured using a SPAD (Soil Plant Analysis Development) meter to provide ground-truth data. A dataset including single-band reflectance, vegetation indices, and texture features was built, and sensitive variables were identified by Pearson correlation. Modeling was performed with linear regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Partial Least Squares Regression (PLSR), and Support Vector Machine (SVM). Results showed that two spectral bands (green and red), one vegetation index (TCARI/OSAVI), and twelve texture features correlated strongly with SPAD values. Among the machine learning models, XGBoost achieved the highest accuracy, demonstrating the effectiveness of integrating multi-feature UAV data for complex olive canopies. This study demonstrates that combining reflectance, vegetation indices, and texture features within the XGBoost model enables reliable chlorophyll estimation for olive canopies, highlighting the potential of UAV-based multispectral approaches for precision monitoring and providing a foundation for applications in other woody crops with complex canopy structures. Full article
(This article belongs to the Special Issue Precision Monitoring of Crops and Pastures Using UAV, Satellite, and Sensor Technologies)
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24 pages, 10480 KB  
Article
Detecting Abandoned Cropland in Monsoon-Influenced Regions Using HLS Imagery and Interpretable Machine Learning
by Sinyoung Park, Sanae Kang, Byungmook Hwang and Dongwook W. Ko
Agronomy 2025, 15(12), 2702; https://doi.org/10.3390/agronomy15122702 - 24 Nov 2025
Cited by 1 | Viewed by 2340
Abstract
Abandoned cropland has been expanding due to complex socio-economic factors such as urbanization, demographic shifts, and declining agricultural profitability. As abandoned cropland simultaneously brings ecological, environmental, and social risks and benefits, quantitative monitoring is essential to assess its overall impact. Satellite image-based spatial [...] Read more.
Abandoned cropland has been expanding due to complex socio-economic factors such as urbanization, demographic shifts, and declining agricultural profitability. As abandoned cropland simultaneously brings ecological, environmental, and social risks and benefits, quantitative monitoring is essential to assess its overall impact. Satellite image-based spatial data are suitable for identifying spectral characteristics related to crop phenology, and recent research has advanced in detecting large-scale abandoned cropland through changes in time-series spectral characteristics. However, frequent cloud covers and highly fragmented croplands, which vary across regions and climatic conditions, still pose significant challenges for satellite-based detection. This study combined Harmonized Landsat and Sentinel-2 (HLS) imagery, offering high temporal (2–3 days) and spatial (30 m) resolution, with the eXtreme Gradient Boosting (XGBoost) algorithm to capture seasonal spectral variations among rice paddy, upland fields, and abandoned croplands. An XGBoost model with a Balanced Bagging Classifier (BBC) was used to mitigate class imbalance. The model achieved an accuracy of 0.84, Cohen’s kappa 0.71, and F2 score 0.84. SHapley Additive exPlanations (SHAP) analysis identified major features such as NIR (May–June), SWIR2 (January), MCARI (September), and BSI (January–April), reflecting phenological differences among cropland types. Overall, this study establishes a robust framework for large-scale cropland monitoring that can be adapted to different regional and climatic settings. Full article
(This article belongs to the Special Issue Precision Monitoring of Crops and Pastures Using UAV, Satellite, and Sensor Technologies)
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