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Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 8362

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Special Issue Editors

Dr. Christoph Jörges

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Guest Editor

Department of Geography, Faculty of Geosciences, Ludwig-Maximilians-Universität München (LMU), Luisenstrasse 37, 80333 Munich, Germany
Interests: machine learning; crop modelling; agriculture remote sensing

Dr. Aaron Moody

E-Mail Website
Guest Editor

Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Interests: environmental conservation; biogeography; remote sensing; quantitative methods in geography; spatial analysis

Special Issue Information

Dear Colleagues,

Earth observation through remote sensing provides an essential source of continuous spatio-temporal data. Artificial intelligence, in turn, can use these big data to gain new insights and information, as well as find correlations and patterns. Nevertheless, these data-driven algorithms sometimes lack interpretability and physical accuracy, which could be enhanced by combining machine learning approaches with process-based physical modeling.

This Special Issue on “Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing” aims to gather high-quality state-of-the-art research contributions on recent applications to support sustainable agricultural practices or new methods for vegetation monitoring, among others.

Manuscript submissions are encouraged to cover a broad range of related topics, including but not limited to the following:

Data- and process-driven model integration for agriculture and vegetation applications;
Physics-informed neural networks (PINNs);
Digital twin with a focus on agriculture/vegetation;
Potentials and limitations of AI algorithms and methods for agriculture/vegetation applications;
AI for agricultural decision making;
Data fusion and super-resolution;
Time series analysis;
Image processing, classification, semantic segmentation, and object detection;
Hyperspectral imaging for agriculture/vegetation (e.g., protein quantification, soil carbon content);
Change detection and agriculture/vegetation monitoring;
Urban heat islands and green spaces;
Drought monitoring;
Pest and disease monitoring;
Smart farming and agriculture.

All proposals related to the application of AI to remote sensing data in agriculture and vegetation will also be evaluated.

Dr. Christoph Jörges
Dr. Aaron Moody
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. Remote Sensing 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 2700 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

artificial intelligence
machine learning
deep learning
remote sensing
data mining
water–energy–food nexus
crop yield prediction
agriculture
climate change
sustainable irrigation and fertilization

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Published Papers (5 papers)

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Research

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32 pages, 6841 KB

Open AccessArticle

Integration of UAV and Remote Sensing Data for Early Diagnosis and Severity Mapping of Diseases in Maize Crop Through Deep Learning and Reinforcement Learning

by Jerry Gao, Krinal Gujarati, Meghana Hegde, Padmini Arra, Sejal Gupta and Neeraja Buch

Remote Sens. 2025, 17(20), 3427; https://doi.org/10.3390/rs17203427 - 13 Oct 2025

Viewed by 653

Abstract

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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23 pages, 10375 KB

Open AccessArticle

Extraction of Photosynthetic and Non-Photosynthetic Vegetation Cover in Typical Grasslands Using UAV Imagery and an Improved SegFormer Model

by Jie He, Xiaoping Zhang, Weibin Li, Du Lyu, Yi Ren and Wenlin Fu

Remote Sens. 2025, 17(18), 3162; https://doi.org/10.3390/rs17183162 - 12 Sep 2025

Viewed by 567

Abstract

Accurate monitoring of the coverage and distribution of photosynthetic (PV) and non-photosynthetic vegetation (NPV) in the grasslands of semi-arid regions is crucial for understanding the environment and addressing climate change. However, the extraction of PV and NPV information from Unmanned Aerial Vehicle (UAV) remote sensing imagery is often hindered by challenges such as low extraction accuracy and blurred boundaries. To overcome these limitations, this study proposed an improved semantic segmentation model, designated SegFormer-CPED. The model was developed based on the SegFormer architecture, incorporating several synergistic optimizations. Specifically, a Convolutional Block Attention Module (CBAM) was integrated into the encoder to enhance early-stage feature perception, while a Polarized Self-Attention (PSA) module was embedded to strengthen contextual understanding and mitigate semantic loss. An Edge Contour Extraction Module (ECEM) was introduced to refine boundary details. Concurrently, the Dice Loss function was employed to replace the Cross-Entropy Loss, thereby more effectively addressing the class imbalance issue and significantly improving both the segmentation accuracy and boundary clarity of PV and NPV. To support model development, a high-quality PV and NPV segmentation dataset for Hengshan grassland was also constructed. Comprehensive experimental results demonstrated that the proposed SegFormer-CPED model achieved state-of-the-art performance, with a mIoU of 93.26% and an F1-score of 96.44%. It significantly outperformed classic architectures and surpassed all leading frameworks benchmarked here. Its high-fidelity maps can bridge field surveys and satellite remote sensing. Ablation studies verified the effectiveness of each improved module and its synergistic interplay. Moreover, this study successfully utilized SegFormer-CPED to perform fine-grained monitoring of the spatiotemporal dynamics of PV and NPV in the Hengshan grassland, confirming that the model-estimated f_PV and f_NPV were highly correlated with ground survey data. The proposed SegFormer-CPED model provides a robust and effective solution for the precise, semi-automated extraction of PV and NPV from high-resolution UAV imagery. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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25 pages, 7899 KB

Open AccessArticle

Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery

by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman and Maxime Leduc

Remote Sens. 2025, 17(10), 1759; https://doi.org/10.3390/rs17101759 - 18 May 2025

Cited by 1 | Viewed by 2002

Abstract

Climate change is threatening the sustainability of crop yields due to an increasing frequency of extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring of field crops. This study aimed to estimate alfalfa crop height through satellite images and machine learning methods within the Google Earth Engine (GEE) Python API. Ground measurements for this study were collected over three years in four Canadian provinces. We utilized Sentinel-2 data to obtain satellite imagery corresponding to the same timeframe and location as the ground measurements. Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). The efficacy of these algorithms has been assessed and compared. Several widely used vegetation indices, for instance normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and normalized difference red-edge (NDRE), were selected and assessed in this study. RF feature importance was utilized to determine the ranking of features from most to least significant. Several feature selection strategies were utilized and compared with the situation where all features are used. We demonstrated that RF and XGB surpassed SVR when assessing test data performance. Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. The analysis of important features showed that normalized difference red edge (NDRE) and normalized difference water index (NDWI) were the most important variables in determining alfalfa crop height. The results of this study also demonstrated that using RF and feature selection strategies, alfalfa crop height can be estimated with comparably high accuracy. Given that the models were fully trained and developed in Python (v. 3.10), they can be readily implemented in a decision support system and deliver near real-time estimations of alfalfa crop height for farmers throughout Canada. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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21 pages, 2750 KB

Open AccessArticle

Comparison of Tree Typologies Mapping Using Random Forest Classifier Algorithm of PRISMA and Sentinel-2 Products in Different Areas of Central Italy

by Eros Caputi, Gabriele Delogu, Alessio Patriarca, Miriam Perretta, Giulia Mancini, Lorenzo Boccia, Fabio Recanatesi and Maria Nicolina Ripa

Remote Sens. 2025, 17(3), 356; https://doi.org/10.3390/rs17030356 - 22 Jan 2025

Cited by 1 | Viewed by 1794

Abstract

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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Review

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27 pages, 5151 KB

Open AccessReview

Advancing Sparse Vegetation Monitoring in the Arctic and Antarctic: A Review of Satellite and UAV Remote Sensing, Machine Learning, and Sensor Fusion

by Arthur Platel, Juan Sandino, Justine Shaw, Barbara Bollard and Felipe Gonzalez

Remote Sens. 2025, 17(9), 1513; https://doi.org/10.3390/rs17091513 - 24 Apr 2025

Cited by 5 | Viewed by 2512

Abstract

Polar vegetation is a critical component of global biodiversity and ecosystem health but is vulnerable to climate change and environmental disturbances. Analysing the spatial distribution, regional variations, and temporal dynamics of this vegetation is essential for implementing conservation efforts in these unique environments. However, polar regions pose distinct challenges for remote sensing, including sparse vegetation, extreme weather, and frequent cloud cover. Advances in remote sensing technologies, including satellite platforms, uncrewed aerial vehicles (UAVs), and sensor fusion techniques, have improved vegetation monitoring capabilities. This review explores applications—including land cover mapping, vegetation health assessment, biomass estimation, and temporal monitoring—and the methods developed to address these needs. We also examine the role of spatial, spectral, and temporal resolution in improving monitoring accuracy and addressing polar-specific challenges. Sensors such as Red, Green, and Blue (RGB), multispectral, hyperspectral, Synthetic Aperture Radar (SAR), light detection and ranging (LiDAR), and thermal, as well as UAV and satellite platforms, are analysed for their roles in low-stature polar vegetation monitoring. We highlight the potential of sensor fusion and advanced machine learning techniques in overcoming traditional barriers, offering a path forward for enhanced monitoring. This paper highlights how advances in remote sensing enhance polar vegetation research and inform adaptive management strategies. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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