AI-Powered Remote Sensing for Agriculture
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 50
Special Issue Editors
Interests: remote sensing; soil moisture; agriculture; machine learning
Interests: deep learning; machine learning; remote sensing
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to this Special Issue, titled “AI-Powered Remote Sensing for Agriculture.” The rapid advancement of artificial intelligence (AI) and remote sensing technologies is optimizing agricultural monitoring and management. Facing global challenges such as climate change, resource scarcity, and food security, the integration of AI with remote sensing observation offers new opportunities for scalable, accurate, and timely agricultural insights. This research area plays a vital role in enhancing sustainability, efficiency, and resilience in agricultural systems.
This Special Issue aims to gather original research and reviews on how AI methods can be integrated with remote sensing to enhance agricultural applications, including machine learning, deep learning, and emerging algorithms, etc. The focus includes both methodological advances and case studies. The scope aligns with the journal’s mission to foster innovative approaches that link technology with environmental and agricultural solutions.
We welcome contributions that combine remote sensing observation with AI to improve crop monitoring, irrigation scheduling, soil and water management, yield forecasting, climate adaptation, and smart agriculture. By providing a platform for interdisciplinary research, this Special Issue will highlight the transformative role of AI-powered remote sensing in ensuring sustainable and resilient agriculture.
Original research articles and comprehensive reviews are invited. Topics may include, but are not limited to, the following:
- AI-driven crop type mapping and growth stage monitoring;
- Yield prediction models based on machine learning and deep learning;
- Detection of agricultural stress (drought, flood, pests, diseases, etc.);
- Integration of multi-source remote sensing (optical, thermal, microwave, etc.) with AI;
- AI-enabled irrigation scheduling and water resource management;
- Climate change impact assessment and agricultural adaptation strategies;
- Spatio-temporal data fusion and assimilation techniques in agriculture;
- Explainable AI and uncertainty quantification in agricultural applications;
- Cases and analyses of AI-driven smart agriculture.
We look forward to receiving your contributions and building a collection of high-quality papers that will advance both academic research and practical innovation in AI-powered agricultural remote sensing.
Dr. Haoxuan Yang
Dr. Xiaofeng Ma
Dr. Mengyuan Xu
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. AI is an international peer-reviewed open access monthly 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 1600 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
- remote sensing
- agriculture
- crop monitoring
- irrigation management
- yield prediction
- digital mapping in agriculture
- machine learning
- deep learning
- precision agriculture
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