Multi-Scale Contextual Swin Transformer for Crop Image Segmentation

Abstract Combining UAV-based remote sensing with deep learning for image segmentation is a particularly innovative and effective technology in modern agriculture. This approach allows for detailed and precise analysis of agricultural fields, such as crop monitoring, yield prediction, and irrigation management, enhancing decision-making and farm management practices. Inspired by the recent advancements of Transformers in computer vision, this paper introduces the Multi-Scale Contextual Swin Transformer (MSC-Swin), a novel model for precise segmentation of UAV crop images. MSC-Swin innovatively combines a Swin Transformer architecture for detailed feature extraction with pooling operations to utilize multi-scale contextual information. Our extensive experimentation demonstrates that MSC-Swin not only achieves state-of-the-art performance on the Barley Remote Sensing dataset, with a record mIoU of 86.4% on the test set, but also exhibits robustness and excellent generalizability.