Application of Hyperspectral Imagery in Precision Agriculture
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: closed (28 February 2025) | Viewed by 11970
Special Issue Editor
Interests: GIS; remote sensing; photogrammetry, structure from motion (SfM), LiDAR; precision agriculture; hyperspectral imaging; spectroscopy; machine learning; PVY potato virus Y; CNN
Special Issue Information
Dear Colleagues,
Precision agriculture leveraging remote sensing will be vital for future global food security. Growers need to be equipped with strategies to detect crop threats and respond with targeted mitigation that is cost-effective and sustainable. This Special Issue provides an opportunity to highlight leading-edge remote sensing research that addresses critical issues facing agricultural systems, such as efficient water use, monitoring croplands for crop pest and disease threats, and developing targeted nutrient applications.
New sensor systems in precision agriculture include high-resolution spectral and spatial hyperspectral sensors aboard UAVs that allow the identification of crop threats at leaf scale by examining their spectral signatures. Machine learning and artificial intelligence applied to these data are opening the doors to the early detection and removal of diseased plants or those infected with viruses or other pathogens. Papers describing hyperspectral sensor applications across agricultural crop types are encouraged for this Special Issue.
Dr. Donna M. Delparte
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- hyperspectral remote sensing
- spectral analysis
- CNN
- crop threats
- crop yield forecasting
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