SGR-YOLO: a method for detecting seed germination rate in wild rice - Yao, Qiong; Zheng, Xiaoming; Zhou, Guomin; Zhang, Jianhua (Frontiers in Plant Science, 2024)
Seed germination rate is one of the important indicators to measure seed quality and seed germination ability, and it is also an important basis for evaluating the growth potential and planting effect of seeds. In order to detect seed germination rate more efficiently and achieve automated detection, this study focuses on wild rice as the research subject. A novel method for detecting wild rice germination rates is introduced, leveraging the SGR-YOLO model through deep learning techniques. The SGR-YOLO model incorporates the convolutional block attention module (ECA) in the Backbone; adopts the structure of bi-directional feature pyramid network (BiFPN) in the Neck part; adopts the GIOU function as the loss function in the Prediction part; and adopts the GIOU function as the loss function by setting the weighting coefficient to accelerate the detection of the seed germination rate. In the Prediction part, the GIOU function is used as the loss function to accelerate the learning of high-confidence targets by setting the weight coefficients to further improve the detection accuracy of seed germination rate. The results showed that the accuracy of SGR-YOLO model for wild rice seed germination discrimination was 94% for hydroponic box and 98.2% for Petri dish. The errors of germination potential, germination index and average germination days detected by SGR-YOLO in hydroponic box and Petri dish with the manual statistics were 0.4%, 2.2 and 0.9d; 0.5%, 0.5 and 0.24d, respectively. The above results showed that that the SGR-YOLO model can realize the rapid detection of germination rate, germination potential, germination index and average germination days of wild rice seeds, which can provide a reference for the rapid detection of crop seed germination rate.