Lightweight Detection of Grape Inflorescences and Fruitlets using an Improved YOLOv8 Model

Globally, grape cultivation spans vast areas and achieves substantial yields, making grapes and related industries vital economic pillars for many nations. In grape production, efficient and precise management during key growth stages is essential for enhancing both yield and quality. In view of the problems that during the grape inflorescences and young fruits stage, the targets are small in size, easily obscured by branches and leaves, and highly similar in color to the background, resulting in poor recognition performance of existing detection methods in complex natural environments, which in turn restricts the application of precision spraying technology. This paper establishes a dedicated dataset for grape inflorescences and young fruits in Xinjiang and proposes an improved lightweight detection model, YOLOv8-FCD. The model incorporates a PConv-based C2f_Faster module to reduce parameter count and computational complexity, replaces the original upsampling method with the CARAFE module to enhance feature extraction capability, and introduces the Detect_SEAM detection head to improve recognition accuracy under occlusion and small-target conditions. Experimental results show that the YOLOv8-FCD model achieves a detection precision (P) of 93.7% and a recall (R) of 87.3%, with a mean average precision (mAP) of 94.6%. Compared to the original YOLOv8n model, P improved by 8.2%, mAP increased by 2.6%, and the model size is reduced to 85.71% of the original. This model provides effective technical support for the identification of grape inflorescences and young fruits in intelligent spraying for plant protection.