Crop and Weed Segmentation. 
The Crop and Weed dataset is adopted, which is a large-scale, multi-modal dataset designed for crop and weed segmentation. The data was collected from multiple commercial agricultural sites and specially cultivated in outdoor experimental plots in Austria, spanning various growing seasons between 2018 and 2021. It covers diverse lighting conditions, weather scenarios, soil types, and combinations of crops and weeds, while also including negative samples with no visible vegetation and backgrounds containing debris. The dataset contains 7,705 images and approximately 112,000 annotated instances. We adopt the Fine24 subvariant of the Crop and Weed dataset as the benchmark (16 crop and 8 weed categories), partitioned into 6,164 training images and 1,541 test images.

Steininger, D., Trondl, A., Croonen, G., Simon, J. & Widhalm, V. The CropAndWeed Dataset: a Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation. in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 3718–3727 (IEEE, Waikoloa, HI, USA, 2023). doi:10.1109/WACV56688.2023.00372.