YOLO-SGD: Precision-Oriented Intelligent Detection of Seed Germination Completion

The seed-germination percentage is an important indicator of the seed viability and growth potential and has important implications for plant breeding and agricultural production. Thus, to increase the speed and accuracy in measuring the completion of germination in experimental seed batches for precise germination percentage calculation, we evaluated a You-Only-Look-Once (YOLO)–Seed Germination Detection (SGD) algorithm that integrates deep-learning technology and texture feature-extraction mechanisms specific to germinating seeds. The algorithm was built upon YOLOv7-l, and its applicability was optimised based on the results of our germination experiments. In the backbone network, an internal convolution structure was substituted to enhance the spatial specificity of the initial features. Following the output of the main feature-extraction network, an Explicit Visual Centre (EVC) module was introduced to mitigate the interference caused by intertwined primary roots from germinated seeds, which can affect recognition accuracy. Furthermore, a Spatial Context Pyramid (SCP) module was embedded after enhancing the feature-extraction network to improve the model’s accuracy in identifying seeds of different scales, particularly in recognising small target seeds. Our results with cabbage seeds showed that the YOLO–SGD model, with a model size of 45.22 M, achieved an average detection accuracy of 99.6% for large-scale seeds and 96.4% for small-scale seeds. The model also achieved a mean average precision and F1 score of 98.0% and 93.3%, respectively. Compared with manual germination-rate detection, the model maintained an average absolute error of prediction within 1.0%, demonstrating sufficient precision to replace manual methods in laboratory environments and efficiently detect germinated seeds for precise germination percentage assessment.