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.

Processing tomatoes, as a major cash crop in China, still face challenges in mechanization and rely heavily on manual labor during production. For instance, the transplanting stage depends on semi-automated equipment, requiring continuous manual seedling placement and replanting, resulting in precision and efficiency that fail to meet the demands of large-scale cultivation. During harvesting, the limited adaptability of sorting mechanisms leads to persistently high rates of impurities in the fruit, hindering commercial processing efficiency. In contrast, while land preparation has achieved preliminary mechanization, deep loosening quality remains suboptimal. Plant protection faces technical bottlenecks in precise pesticide application tailored to processing tomato characteristics. This paper systematically reviews the global and domestic status of mechanized technologies across the entire processing tomato production chain, analyzing the development of land preparation, transplanting, plant protection, and harvesting machinery. Addressing the low mechanization level and uneven development across production stages in China's processing tomato sector, this paper proposes future development should focus on: Tackling key technological challenges and upgrading equipment, deepening the integration of agricultural machinery and agronomic practices, and advancing research on automation and intelligent production machinery. These recommendations aim to provide theoretical support and technical guidance for achieving full mechanization in China's processing tomato production.