A rapid segmentation method for weed based on CDM and ExG index

Precision spraying technology is promising for effective weed control application in agriculture, which is dependent on the perception of weed information. However, existing weed segmentation algorithms have significant limitations in the accurate segmentation of all weed types in real time. In this study, a rapid segmentation method for weed was proposed following the crop detection model (CDM) and the Excess Green Index (ExG). The CDM was introduced, such that the normal convolution in the YOLO-V4 Tiny backbone network was replaced with a depthwise separable convolution to increase the receptive field of the network while reducing the number of parameters. Moreover, the CDM incorporated a SPP structure in YOLO-V4 Tiny to reduce the effect of different scale targets on detection results. After training, the AP value of the CDM was increased to 94.83%, with an inference time of 11.13 ms per image. Subsequently, the CDM was combined with the ExG index and the optimized Otsu to segment the weeds from the maize field accurately and rapidly. As revealed by the experimental results, the proposed algorithm can segment weeds in real time and accurately with a precision of 92.50%, an IoU of 76.14%, as well as a accuracy (Acc) of 98.10%. The segmentation time per image reached 15.40 ms. Lastly, two deployment methods were proposed to make the algorithm conform to different field spraying operation requirements. In brief, the proposed method provides a reliable and efficient solution for weed segmentation in agricultural fields.