T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea-plantation environments.

BACKGROUND: Quick and accurate detection of nutrient buds is essential for yield prediction and field management in tea plantations. However, the complexity of tea plantation environments and the similarity in color between nutrient buds and older leaves make the location of tea nutrient buds challenging. RESULTS: This research presents a lightweight and efficient detection model, T-YOLO, for the accurate detection of tea nutrient buds in unstructured environments. First, a lightweight module, C2fG2, and an efficient feature extraction module, DBS, are introduced into the backbone and neck of the YOLOv5 baseline model. Second, the head network of the model is pruned to achieve further lightweighting. Finally, the dynamic detection head is integrated to mitigate the feature loss caused by lightweighting. The experimental data show that T-YOLO achieves a mean average precision (mAP) of 84.1%, the total number of parameters for model training (Params) is 11.26 million (M), and the number of floating-point operations (FLOPs) is 17.2 Giga (G). Compared with the baseline YOLOv5 model, T-YOLO reduces Params by 47% and lowers FLOPs by 65%. T-YOLO also outperforms the existing optimal detection YOLOv8 model by 7.5% in terms of mAP. CONCLUSION: The T-YOLO model proposed in this study performs well in detecting small tea nutrient buds. It provides a decision-making basis for tea farmers to manage smart tea gardens. The T-YOLO model outperforms mainstream detection models on the public dataset, Global Wheat Head Detection (GWHD), which offers a reference for the construction of lightweight and efficient detection models for other small target crops. © 2024 Society of Chemical Industry.

Comparison of the Effects of Different Forms of Nutrition Education on Adolescent Male Soccer Players.

The purpose of this study was to compare the educational effects on nutrition knowledge of two teaching methods targeting adolescent male soccer players through learning online from WeChat account articles (WeChat group) or taking classroom courses (classroom group). The study investigates whether such teaching methods can improve self-efficacy and nutrition knowledge for athletes. A total of 41 U15 (age 15) youth male soccer players, 21 in the classroom group and 20 in the WeChat group, participated in the experiment by receiving the same nutrition education separately for 12 weeks. An athlete nutrition KAP questionnaire and self-efficacy questionnaire were conducted before the intervention, immediately after the intervention, and 6 weeks and 12 weeks after the intervention. As a result, the nutritional knowledge score and the total score of the athlete nutrition KAP questionnaire in the classroom group increased significantly and were notably higher than those in the WeChat group. Self-efficacy scores improved significantly in both groups. In conclusion, the study showed that the level of nutritional knowledge of U15 male soccer players was mediocre, and both forms of nutrition education can significantly improve the level of nutritional knowledge and self-efficacy of the players. In comparison, the educational effect of classroom teaching is significantly greater and more consistent than that of learning from WeChat public articles.