Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing (Second Edition)
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: 31 October 2026 | Viewed by 454
Editors
Interests: machine learning; advanced statistical analysis; agriculture remote sensing
Special Issues, Collections and Topics in MDPI journals
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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