Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
Problem
Non-destructive, efficient crop monitoring over large areas is needed for precision agriculture, phenotyping, and food-security decisions.
Why it matters
Crop monitoring informs precision agriculture, plant phenotyping, grain policy, and food security decisions.
What the AI did
Machine learning combined canopy spectral information from Worldview-2/3 satellite data with canopy structure features from UAV RGB imagery to predict soybean leaf area index, aboveground biomass, and leaf traits.
Results
not stated in source
Impact
Assessed the potential of satellite/UAV data fusion with machine learning for soybean crop monitoring.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike, Ahmad M. Daloye, H. Erkbol, Felix Fritschi |
| Citations | 305 (OpenAlex, at import time) |
| References | 112 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs12091357 |
| Open access | gold |
| Keywords | crop monitoring, UAV, satellite data fusion, machine learning, soybean, remote sensing |
Importance rating
significant — thousands to millions affected
Abstract states crop monitoring is of great significance for precision agriculture, phenotyping, grain policy, and food security; assessment limited to one heterogeneous soybean field.
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