Applications of Artificial Intelligence Methods to Agroforestry Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 30 January 2026 | Viewed by 6
Special Issue Editors
Interests: agricultural and forestry remote sensing; digital agriculture and forestry; deep learning; computer vision; forest biomass estimation
Interests: remote sensing technology and applications; land use/land cover change (LUCC); forest biomass estimation; impervious surface extraction; deep learning in geospatial analysis
Special Issues, Collections and Topics in MDPI journals
Interests: forestry artificial intelligence; forestry metaverse; forestry remote sensing; computer vision; graphics; multi-source remote sensing fusion
Special Issues, Collections and Topics in MDPI journals
Interests: forestry equipment and informatization; intelligent processing and application of remote sensing big data; regional ecological remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the applications of deep learning to agricultural and forestry remote sensing fields, showcasing cutting-edge methods that bridge technological innovation with practical needs in agricultural and forestry environments. It welcomes original research and review articles on advanced methods for processing and analyzing multi-source remote sensing data. The research data include any remotely sensed data from spaceborne, airborne, and ground-based instruments.
Key topics include crop and tree species classification and monitoring, canopy and understory vegetation segmentation, object detection, pest and disease identification, and crop yield or biomass estimation. The focus is on practical and highly integrated deep learning solutions that effectively address the complex challenges of real-world agricultural and forestry environments. Emphasis is placed on multi-sensor data fusion, spatiotemporal modeling, and lightweight model design. These innovations will enhance system robustness, real-time performance, and deployment flexibility, ultimately enabling intelligent and adaptive decision support to boost agricultural productivity, optimize resource use, and promote sustainable forest management.
This Special Issue offers valuable topics and approaches for researchers and professionals across remote sensing, agronomy, forestry, and environmental sciences, supporting the advancement of knowledge and innovation within this dynamic interdisciplinary domain.
Prof. Dr. Weili Kou
Prof. Dr. Dengsheng Lu
Prof. Dr. Ting Yun
Prof. Dr. Weiheng Xu
Guest Editors
Dr. Shukor Sanim Mohd Fauzi
Guest Editor Assistant
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
- agricultural remote sensing
- forestry remote sensing
- multi-sensor fusion
- deep learning
- computer vision
- crop monitoring
- precision agriculture
- sustainable forest management
- internet of things
- biomass estimation
- algorithm improvement
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