Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant. While the potentials of Convolutional Neural Networks for detection, classification and segmentation tasks have successfully been demonstrated in other application areas, this relatively new field currently lacks the required quantity and quality of training data for such a highly data-driven approach.
Therefore, the researchers from the AIT Center for Vision, Automation & Control (Competence Unit Assistive & Autonomous Systems) propose a novel large-scale image dataset specializing in the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. They provided annotations of labeled bounding boxes, semantic masks and stem positions for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with an extensive set of meta-annotations regarding environmental conditions and recording parameters.
The researchers furthermore conduct benchmark experiments for multiple learning tasks on different variants of the dataset to demonstrate its versatility and provide examples of useful mapping schemes for tailoring the annotated data to the requirements of specific applications. In the course of the evaluation, they furthermore could demonstrate how incorporating multiple species of weeds into the learning process increases the accuracy of crop detection. Overall, the evaluation clearly demonstrates that the dataset represents an essential step towards overcoming the data gap and promoting further research in the area of Precision Agriculture.
Check the publication:
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 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2023, pp.3729-3738.