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AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

Pytorch Psetae ⭐ 98

PyTorch implementation of the model presented in "Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention"

Utae Paps ⭐ 71

PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

Autonomous Farm Robot ⭐ 37

UG Project 2019-20.

Cropmask_rcnn ⭐ 14

Segmenting center pivot agriculture to monitor crop water use in drylands with Mask R-CNN and Landsat satellite imagery

Dmil Wdds ⭐ 9

Deep multiple instance learning based wheat diseases diagnosis system

Mask_r_cnn_in_fruit_counting ⭐ 6

Applications of AI and Computer Vision in Agriculture-Fruit recognition, localization and segmentation

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