Remote Sensing and Machine Learning for Crop Phenotyping and Structural Analysis
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 254
Editors
Interests: image recognition (computer vision technology) and intelligent monitoring of crop growth; crop growth simulation and its system design; UAV phenotypic monitoring and data analysis; the design and application of agricultural Internet of Things systems
Special Issues, Collections and Topics in MDPI journals
Interests: LiDAR; precision agriculture; structure from motion; machine learning; se-mantic analysis; segmentation and classification; biomass estimation; deep learning
Special Issue Information
Dear Colleagues,
Accurate characterization of crop structural traits and field-scale phenotypes is essential for advancing precision agriculture and sustainable crop management. Recent developments in remote sensing technologies and machine learning methods enable the robust extraction of crop architectural features and spatial patterns across diverse agroecosystems. Unmanned Aerial Vehicles, LiDAR, multispectral and hyperspectral imaging and other remote sensing platforms provide multi-modal datasets for high-throughput phenotyping and structural analysis.
This Special Issue focuses on scalable, learning-based approaches for crop phenotyping and structural analysis using remote sensing data. Contributions are welcome on methods for semantic segmentation, multi-modal data integration, data-efficient machine learning, multi-temporal analysis and structural trait extraction. Studies emphasizing generalizability, robustness and cross-environment applicability are particularly encouraged.
We invite original research articles, methodological studies and reviews that integrate machine learning, remote sensing and agronomic applications to advance our understanding of crop phenotyping and structural variability. Unmanned Aerial Vehicles-based data are highlighted as an important, but not exclusive, component of multi-source phenotyping systems.
Prof. Dr. Chengming Sun
Guest Editor
Dr. Fei Zhang
Guest Editor Assistant
Manuscript Submission Information
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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. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- crop phenotyping
- crop structural analysis
- remote sensing
- Unmanned Aerial Vehicles imagery
- LiDAR
- multi-modal data integration
- machine learning
- semantic segmentation
- multi-temporal analysis
- data-efficient methods
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