Abstract:Accurate detection and identification of weeds is a prerequisite for weed control. Aiming at the problem of low accuracy of weed segmentation in complex field environment, an intelligent weed detection and segmentation method based on Mask RCNN was proposed. The ResNet101 network was used to extract the feature map of weed semantic and spatial information. The characteristic map was classified by the regional suggestion network, and the preselection box regression was trained. The preselection area was screened by the nonmaximum suppression algorithm. RoIAlign was used to cancel the border position deviation caused by quantization, and the region of interest (RoI) feature map was transformed into a fixedsize feature map. The output module calculated the classification, regression and segmentation loss for each RoI, predicted the category, location and contour of the candidate area through training, and realized weed detection and contour segmentation. When IoU (intersection over union) was 0.5, the mean accuracy precision (mAP) value was 0853, which was better than that of SharpMask and DeepMask with 0.816 and 0.795, respectively. The single sample time of the three methods was 280ms, 256ms and 248ms respectively. The results showed that the method can quickly and accurately detect and segment the category, location and contour of weeds, and it can be better than SharpMask and DeepMask. When IoU was 0.5, the mAP value of the proposed method was 0.785, and the time for a single sample was 285ms, indicating that this method can realize the field operation in the complex background and meet the realtime control requirements of field pesticide variable spraying. In the field variable spraying test, the accuracy rate of identifying weeds was 91%, the accuracy rate of identifying weeds and spraying them accurately was 85%, the spray density of pesticide spray droplets was 55 per square centimetre, and the average processing time of the device was 0.98s. It can meet the control standard of pesticide variable spraying.