Lightweight detection algorithm of seed potato eyes based on YOLOv5

Potatoes have been a versatile food and cash crop to ensure food security and grain planting structure in China. The total planting area has declined in the planting industries at present. Some challenges also remain in the mechanization of the potato planting industry. The current process of seed potato cutting can rely heavily on manual and mechanical blind cutting, leading to labor-intensive and inefficient tasks. Moreover, there is a high rate of blind cuts and significant loss of seed potatoes. Therefore, it is highly urgent to accurately and rapidly identify the potato eyes before cutting. In this study, an improved model was proposed to detect the potato bud eyes using YOLOv5. Dutch 15 potato variety was taken as the experimental material. The high-quality samples of seed potatoes were carefully chosen to be free of diseases, dry rot, disease spots, and worm eyes. The dataset was used for training, verification, and testing. 1 400 pictures of seed potatoes were obtained with a ratio of 8:1:1. Data expansion techniques (such as mirroring, rotating, cropping, and adjusting brightness) were applied to enhance the dataset, thus resulting in a total of 5 600 images. Since the features of seed potato eyes were relatively simple, there was a decrease to even disappear after multiple convolutions. C3 Faster was integrated into the original framework. While the extraction of sprout eye features was enhanced to reduce the parameters. Additionally, the GD structure was incorporated from the Neck component of GOLD-YOLO to improve the detection accuracy of sprout eye. The bounding box loss function CIoU Loss was replaced with WIoU Loss to expedite the convergence of the network model for high detection accuracy. The hyperparameters of the original YOLO model were optimized for the COCO dataset. There were significant differences from the dataset of seed potato sprout eye in this experiment. Therefore, a genetic algorithm (GA) was employed to fine-tune the hyperparameters specifically for the detection of seed potato sprout eye at the end of the experiment. Furthermore, pruning and distillation techniques were used to reduce the running parameters and memory consumption, in order to make the model more suitable for the detection tasks of seed potato bud eye. The optimized model was presented with a size of 8.7 MB, which was only 61.3% of the original model. Params comprised approximately 57.1% of the original model. The final average accuracies of detection were 90.5% and 90.1%, respectively, in the test and validation sets of the self-made potato dataset. Compared with the lightweight networks YOLOv7-tiny, YOLOv8n, YOLOv5n, and YOLOv5s, the average accuracies of the improved model were 0.5, 1.3, 2.8, and 1.1 percentage points higher, respectively, in the test set of seed potato dataset. The average accuracy of the verification set was 2.9, 1.9, 3.2, and 1.6 percentage points higher. The detection speed on a local computer reached 27.5 frames per second, fully meeting the real-time requirements. Substantial benefits were offered to significantly enhance the efficiency and accuracy of seed potato bud eye detection in the potato cultivation industry.