Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentation in Aerial Agricultural Images

Deep learning and pattern recognition in smart farming has seen rapid growth as a building bridge between crop science and computer vision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture Vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture Vision dataset and outperforms various approaches in literature.