## 4 Discussion  
YOLOv8 performs better than YOLOv5 in tea bud-detection tasks. However, our research team has also noticed that the YOLOv8 model did not successfully identify all tea buds, mainly due to the following three factors:  
1. The morphological characteristics of tea buds affect detection results [21]. Tea buds are small in size and grow in high density, and their color is not significantly different from the background. These factors reduce the visibility and recognizability of tea buds in images, thus increasing the difficulty of the detection algorithm. 2. Factors related to image acquisition also interfere with recognition results [22; 23; 24; 25; 26]. When collecting tea images, it is difficult to maintain a stable distance and shooting angle between the camera and the tea. Besides the variation in the growth direction of tea buds, many other variables can affect the appearance of tea buds in images to some extent, thereby adversely affecting the accuracy of the recognition algorithm. 3. The limitations of the YOLO model are also one of the reasons why some tea buds are not successfully identified. Although the performance of YOLOv8 has improved compared to that of YOLOv5, there are still some challenges when dealing with detection of small targets similar to the surrounding environment. This problem may \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & **Number of** & **Number of** & **Number of** & **Number of** & **Overall** & **Picking Time** \\
**Number** & **Shoots** & **Detections** & **Locations** & **Pickings** & **Success Rate** & **(s)** \\ \hline
1 & 25 & 21 & 18 & 15 & 60.00\% & 29.6 \\
2 & 23 & 20 & 16 & 14 & 60.87\% & 26.5 \\
3 & 33 & 28 & 25 & 22 & 66.67\% & 36.3 \\
4 & 29 & 24 & 22 & 20 & 68.67\% & 34.8 \\
5 & 18 & 15 & 13 & 10 & 55.56\% & 19.8 \\
6 & 27 & 24 & 20 & 16 & 59.26\% & 32.4 \\
7 & 25 & 21 & 17 & 15 & 60.00\% & 28.8 \\
8 & 17 & 14 & 11 & 10 & 58.82\% & 18.7 \\
9 & 26 & 23 & 19 & 16 & 61.84\% & 28.6 \\
10 & 35 & 29 & 25 & 22 & 62.86\% & 42.3 \\ Total & 258 & 219 & 186 & 160 & 62.02\% & 297.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of tender shoots and picking time in different stages of the field picking experiment. stem from the limitations of the model in feature extraction and discrimination ability, which needs to be optimized and improved in future research. In field experiments, we also observed differences in the growth period between the collected tea buds and the samples used in the training model. This difference leads to subtle changes in the color and appearance of tea buds, thereby reducing the confidence of the tea bud-detection model. To address this issue, we suggest that future research should focus on using transfer learning to improve the model's generalization ability for tea bud detection at different growth stages [27]. Additionally, during the positioning of tea buds, some tea buds are severely occluded, leading to positioning errors or failures. Nevertheless, these occluded buds still retain their morphological characteristics. Therefore, future research can consider utilizing these morphological characteristics to establish a topological model of tea buds to improve positioning accuracy. Meanwhile, we must acknowledge some potential limitations of the current method. Firstly, our method may not be robust enough for interference factors in unstructured environments, such as breeze. These factors have a significant impact on the process of picking high-quality tea, especially during camera acquisition, where the movement of tea bud positions can severely affect the accuracy of detection and positioning. To reduce these interferences, we plan to adjust camera parameters based on experimental conditions or add equipment such as hoods to improve the acquisition effect in the future. Another limitation lies in the precision and speed of the robotic arm. The robotic arm plays a crucial role in tea bud picking.