Instance segmentation method for weed detection using UAV imagery in soybean fields

Abstract

Weed detection in crops is a new frontier of precision agriculture, which will enable the distinction between desirable and undesirable plants. Accurate and efficient weed detection and discrimination are fundamental for precision weed management. This study presents a novel approach combining the visible colour index with the instance segmentation method based on the encoder and decoder architecture. This approach tackles the challenge of accurately detecting and segmenting weeds amidst the dense distribution of weeds and soybean crops. The colour index was designed to highlight the contrast between plants and soil to mitigate the effects of illumination and background, while the integration of the ResNet101_v and DSASPP in the encoder-decoder architecture served to reinforce the extraction of abundant multi-scale semantic information to elevate the accuracy of weed patch boundary segmentation. Experimental results on real-world field imagery indicated that the proposed approach exhibited superior performance with an accuracy of 0.905 and an IoU score of 0.959 for weeds segmentation, an aAcc of 0.978, mIoU of 0.939 and a mAcc of 0.972 for overall performance. In comparison with Deeplabv3plus, Deeplabv3, FCN, U-net, FastFCN, Swin Transformer, and Vision Transformer, the proposed pipeline achieved comparable performance in terms of segmentation precision and execution time for differentiating between weeds and soybeans. This work represents a noteworthy contribution to the field of precision agriculture, offering a promising approach to the challenging task of weed detection and precision weed control.