Feature-level fusion of Sentinel-1 SAR and Sentinel-2 optical imagery for crop-type mapping in humid tropical mountainous terrain
融合Sentinel-1 SAR与Sentinel-2光学特征，在印尼热带山地实现93%精度的作物类
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农业人工智能 作物识别
Systematic vegetation index discovery for phenology-based crop mapping
系统生成并评估大量植被指数，用于基于物候的农作物分类，发现优于NDVI的指数。
Reducing redundancy in multi-temporal analysis to improve field-scale detection of crop residue burning
用多时相遥感与模糊机器学习减少重复检测，提升稻田秸秆焚烧田块尺度制图精度。
DINOv3 meets YOLO26 for enhanced crop-weed detection toward precision vegetable weeding
结合DINOv3与YOLO26提升蔬菜田间作物与杂草检测精度，助力精准除草。