Addressing the challenges of complex background interference, low lighting conditions, small target recognition, and difficulty in maturity grading in the automated detection of Pleurotus ostreatus, this study proposes a lightweight improved scheme based on color feature enhancement. By collecting 4779 images from five developmental stages in three typical planting environments, including greenhouses and mushroom houses, an HSV hue analysis database was established to determine key hue intervals [4°, 38°] or [110°, 155°] for different environments. Secondly, based on the hue interval distribution of Pleu-rotus ostreatus, YOLOv13 was used as the base model, with the addition of an HSV hue mask as the fourth channel to improve the input layer. The custom ColorWeight module was used to enhance color feature expression; the hypergraph computation module was improved to enhance feature correlation; and the neck network incorporated the StockenAttention module to improve the ability to capture maturity features. The accuracy of the improved model was increased to 89.5% in mAP@0.5 (+3.3%), surpassing the mainstream YOLOv8n-12n series. Efficiency optimization achieved real-time detection at 12.58 FPS on the RTX3090Ti platform. In practical applications, the accuracy of maturity recognition was significantly improved, with a 73.6% decrease in the misclassification rate of maturity and a reduction in missed detections, achieving an F1 score of 0.91. In conclusion, through the deep integration of Hue features and deep learning models, while ensuring lightweight deployment (with only a 10.5% increase in parameter count), the accuracy and practicality of Pleurotus ostreatus detection were significantly improved, providing an effective solution for intelligent mushroom house management.

The international research trend of tomato molecular breeding was studied based on thesis analysis in SCIE. Through the analysis of the annual number of articles, reveals the variation trend of related research. By the analysis of Scientific productivity and achievement influence, compares the research competitiveness among countries. Use the co-word clustering to identify 6 important research topics in the field, and use the strategy coordinates to evaluation the relative position of these topics .The result shows that, tomato molecular breeding research presents a rising trend. The United States plays an absolute leading role in the field. China’s research layouts more but achievements influence is relatively low. # 5 (tomato genome) and # 1 (resistance) themes are the center of tomatoes molecular breeding. # 2 (oxidative stress) may be the center of the future research.