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Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing (Second Edition)

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2612

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Editors

Dr. Christoph Jörges

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Guest Editor

Department of Geography, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
Interests: machine learning; advanced statistical analysis; agriculture remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Aaron Moody

E-Mail Website
Guest Editor

Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Interests: environmental conservation; biogeography; remote sensing; quantitative methods in geography; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce the launch of the second edition of our Special Issue, titled “Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing (Second Edition)”, in Remote Sensing.

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 in a so-called hybrid modeling framework.

This second Special Issue on “Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing (Second Edition)” 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;

- Hybrid modeling;

- Physics-informed neural networks (PINNs);

- Digital twins with a focus on agriculture/vegetation;

- Potentials and limitations of AI algorithms and methods for agriculture/vegetation applications;

- AI for agricultural decision making, yield predictions, and food security;

- 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;

- 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 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-anonymized 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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Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing in Remote Sensing (12 articles)

Published Papers (4 papers)

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Research

30 pages, 27296 KB

Open AccessArticle

Raspberry Pi Lightweight Deep Learning Models Deployed for Corn Growth Stage Classification Using PhenoCam Images

by Astina Joice, Humeera Tazeen, Talha Tufaique, Mathala Juliet Gupta, C. Igathinathane, Nitin Rai, Craig W. Whippo and David W. Archer

Remote Sens. 2026, 18(19), 3337; https://doi.org/10.3390/rs18193337 - 29 Sep 2026

Viewed by 405

Abstract

Identifying crop growth stages is essential for comprehensive assessment that facilitates timely and precise agronomic management. However, conventional field-based manual scouting is labor-intensive, time-consuming, and lacks scalability. Digital field images with advanced models deployed on portable edge-computing devices offer an alternative solution for real-time crop monitoring and decision support. Therefore, in this study, the standard 8 corn growth stages were defined as 8 classes based on distinct visual canopy appearance patterns observed in PhenoCam (near-surface [proximal] remote sensing network camera) imagery collected from 10 PhenoCam sites. These classes do not correspond directly to standard agronomic vegetation and reproductive growth stages. For modeling, four lightweight deep learning (DL) models, ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2, were developed and evaluated across five image vertical clipping levels (0–40%) using a supercomputer (NDSU CCAST). The optimized model was subsequently deployed on a single-board Raspberry Pi 5 computer for edge inference. Model training accounted for the majority of the total CPU time, exceeding 97%, while the testing times ranged from 0.01–0.12 min, enabling real-time applications. Among the models, ELiteCrop0 achieved the most balanced performance with a confusion-matrix diagonal ratio (CMDR) of 0.93, followed by ELiteCrop1 (CMDR = 0.92). Overall, model performance decreased with increasing vertical clipping; therefore, a moderate image clipping (0–10%) was recommended for improved computational efficiency. Analysis with a supercomputer produced an intrasite (same training sites) accuracy of 0.90–0.93 (Raspberry Pi: 0.78–0.81) and an intersite (unseen test sites) accuracy of 0.48–0.50 (Raspberry Pi: 0.41–0.43), indicating challenges with model generalization. Raspberry Pi processed ≈ 1000 images/min at 68 °C, with a PhenoCam:Raspberry Pi processing ratio of 1:2500 at 50% capacity over an 8 h day. Overall, this study presents a scalable and cost-effective solution for real-time corn growth stage monitoring in precision agriculture, which could be extended to other crops. Full article

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

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21 pages, 11576 KB

Open AccessArticle

Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics

by Sabah Sabaghy, Mohammad Abuzar, Steve Sinclair, Tony Dugdale, Vanessa Hutchins, Yogendra Karna, Jonathan Wilson and Kathryn Sheffield

Remote Sens. 2026, 18(18), 3150; https://doi.org/10.3390/rs18183150 - 13 Sep 2026

Viewed by 379

Abstract

Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands. Full article

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

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26 pages, 18571 KB

Open AccessArticle

A Comprehensive Machine Learning Approach for Crop Classification Using Multi-Sensor Satellite Datasets and Multiple Vegetation Indices

by Oybek Tukhtamishov, Mohamed Fawzy, Karem Abdelmohsen, Arpad Barsi, Rustambek Kodirov, Lorant Foldvary and Zokhid Mamatkulov

Remote Sens. 2026, 18(15), 2571; https://doi.org/10.3390/rs18152571 - 4 Aug 2026

Viewed by 767

Abstract

Accurate crop classification is essential for sustainable agriculture activities and food security studies. Recent advancements in remote sensing data acquisition and analysis techniques enable various solutions for cropland detection; however, reliable crop maps are still lacking in many heterogeneous semi-arid regions (e.g., Central Asia). Machine learning approaches address such challenges and distinguish different crop types using multiple datasets. The main aim of this study is to optimize crop classification outcomes by integrating multi-sensor datasets leveraging numerous vegetation indices through different machine learning models. Four datasets: Landsat-8 (DS-1), Sentinel-2 (DS-2), optical Sentinel-2 integrated with SAR Sentinel-1 (DS-3), and Sentinel-1 (DS-4) were used for the developed experiments. Five vegetation indices, NDVI, GNDVI, EVI, SAVI, and MSAVI, were derived using Sentinel-2 and Landsat-8 bands; in addition, NDRE was only obtained for Sentinel-2 exploiting the red edge band. Three input scenarios were considered for model training and image classification, featuring solely NDVI and its related bands; a set of vegetation indices and their associated bands for optical imagery; and VV, VH, and VV/VH ratio bands for SAR data. Five classifiers, Gradient Boosting Tree (GBT), Random Forest (RF), K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Minimum Distance (MD), were employed to assess the machine learning quality for scene classification. Findings demonstrated that Sentinel-2 outperforms Landsat-8 images due to the higher spatial resolution and red edge bands. DS-3 consistently outperforms both DS-2 (optical-only) and DS-4 (SAR-only) across all classifiers, enhancing the overall accuracy up to 2.38% over the optical dataset, and up to 13.28% over the SAR data, demonstrating the added details on canopy spectral reflectance, structure and moisture content. Using multiple vegetation indices consistently improves performance over NDVI alone across DS-1, DS-2, and DS-3, with gains reaching up to 96.22% due to the complementary information captured by multi-index spectral sensitivity. The GBT and RF classifiers consistently achieved the highest classification performance, effectively combining multiple decision trees to capture complex nonlinear relationships and decision boundaries; meanwhile, the MD classifier exhibited the lowest accuracy due to its reliance solely on distances to class mean vectors. All in all, the presented approach offers a robust framework for crop classification supplemented with multiple data sources using different VI feature scenarios and variable machine learning tools for precise farming applications in semi-arid regions. Full article

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

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23 pages, 43286 KB

Open AccessArticle

Bridging Deep Learning and Ecological Interpretability: A Spatial Mamba Framework for NDVI Prediction in Forest-Steppe Ecotones Under Climate Variability

by Haoran Huang, Yuhang Jiang, Xiaoyan Xu, Xinbai Ouyang, Zirui Guo, Shaowei Ning, Yuliang Zhou, Lei Luo and Juliang Jin

Remote Sens. 2026, 18(13), 2120; https://doi.org/10.3390/rs18132120 - 1 Jul 2026

Viewed by 603

Abstract

Forest-steppe ecotones exhibit pronounced spatiotemporal heterogeneity and complex climate–vegetation interactions, posing significant challenges for vegetation dynamics prediction. Existing models often struggle to capture long-range temporal dependencies, preserve spatial continuity across heterogeneous transition zones, and provide ecologically interpretable insights. To address these limitations, we developed a bidirectional Geo-Spatial Mamba (Geo-S-Mamba) architecture with a multi-objective loss function incorporating spatial continuity constraints based on the first law of geography. The model was trained using multi-source geospatial datasets and independently validated during 2019–2023. The results show that Geo-S-Mamba achieved an R² of 0.93. Moreover, both the bidirectional mechanism and the spatial-continuity loss improved the PSDI by approximately 0.08. The model effectively captured annual variations in NDVI and covariation among vegetation groups. Post hoc symmetric causal learning based on Pearl’s structural causal theory indicated that precipitation was the primary driver of grassland vegetation dynamics. Temperature and radiation influenced NDVI mainly through boundary-dependent effects. Overall, this framework can estimate changes in the spatial distribution of plant communities across heterogeneous environments and provides a scientific basis for further research on forest–steppe ecotones. Full article

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

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