YOLO-SCA: A Lightweight Potato Bud Eye Detection Method Based on the Improved YOLOv5s Algorithm

Bud eye identification is a critical step in the intelligent seed cutting process for potatoes. This study focuses on the challenges of low testing accuracy and excessive weighted memory in testing models for potato bud eye detection. It proposes an improved potato bud eye detection method based on YOLOv5s, referred to as the YOLO-SCA model, which synergistically optimizing three main modules. The improved model introduces the ShuffleNetV2 module to reconstruct the backbone network. The channel shuffling mechanism reduces the model’s weighted memory and computational load, while enhancing bud eye features. Additionally, the CBAM attention mechanism is embedded at specific layers, using dual-path feature weighting (channel and spatial) to enhance sensitivity to key bud eye features in complex contexts. Then, the Alpha-IoU function is used to replace the CloU function as the bounding box regression loss function. Its single-parameter control mechanism and adaptive gradient amplification characteristics significantly improve the accuracy of bud eye positioning and strengthen the model’s anti-interference ability. Finally, we conduct pruning based on the channel evaluation after sparse training, accurately removing redundant channels, significantly reducing the amount of computation and weighted memory, and achieving real-time performance of the model. This study aims to address how potato bud eye detection models can achieve high-precision real-time detection under the conditions of limited computational resources and storage space. The improved YOLO-SCA model has a size of 3.6 MB, which is 35.3% of the original model; the number of parameters is 1.7 M, which is 25% of the original model; and the average accuracy rate is 95.3%, which is a 12.5% improvement over the original model. This study provides theoretical support for the development of potato bud eye recognition technology and intelligent cutting equipment.