An accurate recognition and localization algorithm for strawberry picking robots

With the development of smart agriculture, automatic picking robots have become a research hotspot and achieving accurate recognition of fruits and low-cost deployment are the key technologies. A lightweight strawberry detection model YOLOv10-SD (strawberry detector) is proposed to address the problem that strawberry targets are small and susceptible to occlusion by branches and leaves. Firstly, StarNet is used to reconstruct the backbone network of YOLOv10n to reduce the number of parameters and achieve fast detection. Then, the Coordinate Attention (CA) module is introduced to enhance the accurate recognition of the region of interest or the position of important objects in the convolutional neural network, thereby improving the detection effect of strawberries under complex backgrounds. Finally, this model integrates Inner-IoU into CIoU as the loss function, accelerates the regression process of low IoU samples through a larger auxiliary bounding box, and is conducive to enhancing the detection ability of small target strawberries. Experiments are carried out on a strawberry dataset under a natural growth environment. The results show that compared with the current mainstream object detection algorithms YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv9-c and YOLOv10n, YOLOv10-SD exhibits better comprehensive performance in terms of model parameter quantity, real-time performance and detection accuracy. The YOLOv10-SD model proposed in this study improves the ability to identify and locate strawberries in natural environments while ensuring lightweight, providing a technical reference for efficient smart strawberry picking.