Artificial Intelligence-Based Remote Sensing for Crop Information Extraction and Status Monitoring
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: 29 August 2025 | Viewed by 22
Special Issue Editor
Interests: remote sensing; agriculture; crop modeling; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Agriculture is a cornerstone of the global economy, and with the world’s population growing rapidly, the demand for efficient and sustainable agricultural practices is more urgent than ever. Accurate and timely monitoring of crop conditions is essential to ensure food security, optimize resource use, and enhance productivity. Artificial Intelligence (AI), combined with remote sensing technologies, has revolutionized how we extract and analyse crop information, offering innovative solutions for yield monitoring and precision agriculture.
AI-based techniques provide advanced capabilities to interpret large-scale, multi-source remote sensing data with high accuracy and efficiency. These approaches enable automated extraction of critical crop information, including crop classification, phenology tracking, yield estimation, and early detection of stress factors such as drought, pests, and diseases. The integration of AI with satellite and proximal sensing data opens new possibilities for real-time monitoring, predictive modelling, and decision-making in agricultural systems.
Dr. Muhammad Moshiur Rahman
Guest Editor
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Keywords
- crop growth and vigour monitoring
- crop phenology
- crop diseases and pests
- drought stress
- machine learning
- artificial intelligence
- image processing
- crop classification
- yield prediction
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