Deep‐learning‐based automatic evaluation of rice seed germination rate.

BACKGROUND: Rice is an important food crop plant in the world and is also a model plant for genetics and breeding research. The germination rate is an important indicator that measures the performance of rice seeds. Currently, solutions involving image processing techniques have substantial challenges in the identification of seed germination. The detection of rice seed germination without human intervention involves challenges because the rice seeds are small and densely distributed. RESULTS: In this article, we develop a convolutional neural network (YOLO‐r) that can detect the germination status of rice seeds and automatically evaluate the total number of germinations. Image partition, the Transformer encoder, a small target detection layer, and CDIoU loss are exploited in YOLO‐r to improve the detection accuracy. A total of 21 429 seeds were collected, which have different phenotypic characteristics in length, shape, and color. The results show that the mean average precision of YOLO‐r is 0.9539, which is higher than the compared models. Moreover, the average detection time per image of YOLO‐r was 0.011 s, which meets the real‐time requirements. The experimental results demonstrate that YOLO‐r is robust to complex situations such as water stains, impurities, awns, adhesion, and so on. The results also show that the mean absolute error of the predicted germination rate mainly exists within 0.1. CONCLUSIONS: Numerous experimental studies have demonstrated that YOLO‐r can predict rice germination rate in a fast, easy, and accurate manner. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]