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As technology continues to advance, the automation of strawberry production and picking is an inevitable trend. To address the pressing issues of insufficient detection of smaller strawberries and misdetection resulting from the intricate background of strawberry images, an improved strawberry recognition method based on the YOLOv8n model was proposed. The improvements are as follows: (1) The deletion of the 20 &#x00D7; 20 pixel detection layer with a focus on small target strawberries and the addition of a 160 &#x00D7; 160 pixel small target detection layer were implemented to reduce the model volume and enhance the network reconstruction. (2) In the neck portion, a global attention mechanism was incorporated. (3) The SPD-Conv method was applied to improve the detection capability of small target strawberries. (4) To address the limitations of the CIOU loss function, the EIOU loss function was utilized. The results of the experiment conducted on the self-made strawberry dataset demonstrated that the volume of the improved algorithm model was reduced by 59.7%, its precision was improved by 1.3%, and its recall rate increased by 2.1%. Additionally, the mAP was enhanced by 1.6%. The detection time for a single strawberry fruit image on a GPU was 17. 2 ms, which rendered the improved model suitable for practical applications. The model was verified in terms of small targets, and it achieved better detection performance than yolov5n, yolov6n, and yolov8s. The proposed algorithm demonstrated improved detection capabilities, reduced model size, and better target detection of strawberries.

A Small Target Strawberry Recognition Method Based on Improved YOLOv8n Model