A100：面向农业植物图像分析的混合视觉基础模型
构建混合视觉基础模型A100，用百万级田间图像自监督预训练实现少标注作物图像分析。
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农业人工智能 作物分类
同时包含 农业人工智能、作物分类 的农业资讯，共 11 条。
Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）
构建架构多样性TabNet集成，用多源土壤数据分类7类作物。
Hybrid transformer and CNN for hyperspectral image classification with spectral band selection
提出结合波段选择与ViT-CNN混合框架的高光谱图像分类方法，提升小样本分类精度。
Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy
提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。
Improving early season crop type mapping using machine learning algorithms with combined SAR and optical time series
融合Sentinel-1 SAR与Sentinel-2光学时序，用集成机器学习实现印度早季作物类型高
Supporting Digital Agriculture Through GeoAI : A Crop Type Dataset of Four Food Basket Crops in Rwanda
在卢旺达四区采集四种主粮作物的GPS标注地块数据集，支撑GeoAI作物监测。
Towards accessible smallholder crop classification in the groundnut basin of Senegal
评估地理空间基础模型嵌入在塞内加尔花生盆地小农作物分类中的性能、时序迁移性和可及性。
A hybrid PCA and 1D-CNN approach with regional priors for accurate agricultural monitoring: a case study with EuroCropsML
A hybrid PCA and 1D-CNN approach with regional priors for accurate agricultural monitoring: a case study with EuroCropsML。Precision Agriculture
Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image
提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。
SAF-CropNet: Spatially adaptive fusion of SAR and optical imagery for semantic segmentation in operational cropland mapping across regions
提出SAF-CropNet，用空间自适应融合SAR与光学影像做跨区域耕地语义分割。
An interpretable deep embedding framework for data-driven discovery of crop families
An interpretable deep embedding framework for data-driven discovery of crop families。Smart Agricultural Technology