RESEARCH ON MAIZE STEM RECOGNITION BASED ON MACHINE VISION

ABSTRACT

Fertilization at the large bell stage of maize is the key to increasing maize yield and improving fertilizer use efficiency. To achieve fast and accurate recognition of maize stems by intelligent agricultural equipment in complex field environments, an improved YOLO v4 maize stem recognition model with an increased CBAM, which can achieve real-time identification and positioning of maize stems, is proposed. In this paper, first, we collected images of maize stems under different conditions in the field, expanded the maize stem images and produced a maize stem image dataset by adding Gaussian noise, changing the brightness and performing other data enhancement methods, and manually annotated the maize stem via LabelImg software. Second, a convolutional block attention module (CBAM) and SIoU loss function were added to the original YOLO v4 target detection network to obtain the CB-YOLO v4 target detection network. Last, this network was compared with the original YOLO v4, Faster-RCNN, SSD and YOLO v3 target detection networks, and it achieved 93.1%, 92.4% and 92.6% precision, recall and mAP (mean average precision), respectively, for maize root recognition, which is significantly better than the other algorithms and is suitable for practical maize interrow operation systems.

deep learning; maize stem; YOLO; attention mechanism; loss function