Detection of Seed Potato Sprouts Based on Improved YOLOv8 Algorithm

Seed potatoes without sprouts usually need to be manually selected in mechanized production, which has been the bottleneck of efficiency. A fast and efficient object recognition algorithm is required for the additional removal process to identify unqualified seed potatoes. In this paper, a lightweight deep learning algorithm, YOLOv8_EBG, is proposed to both improve the detection performance and reduce the model parameters. The ECA attention mechanism was introduced in the backbone and neck of the model to more accurately extract and fuse sprouting features. To further reduce the model parameters, Ghost convolution and C3ghost were introduced to replace the normal convolution and C2f blocks in vanilla YOLOv8n. In addition, a bi-directional feature pyramid network is integrated in the neck part for multi-scale feature fusion to enhance the detection accuracy. The experimental results from an isolated test dataset show that the proposed algorithm performs better in detecting sprouts under natural light conditions, achieving an mAP0.5 of 95.7% and 91.9% AP for bud recognition. Compared to the YOLOv8n model, the improved model showed a 6.5% increase in mAP0.5, a 12.9% increase in AP0.5 for bud recognition, and a 5.6% decrease in the number of parameters. Additionally, the improved algorithm was applied and tested on mechanized sorting equipment, and the accuracy of seed potato detection was as high as 92.5%, which was sufficient to identify and select sprouted potatoes, an indispensable step since only sprouted potatoes can be used as seed potatoes. The results of the study can provide technical support for subsequent potato planting intelligence.