The realization of automatic identification and positioning of tea buds is the basis for the development of high-quality tea intelligent picking equipment. Aiming at the problems of tiny tea buds and the picking environment which is greatly affected by light， this research proposes a tea bud recognition method based on a deep learning network model to carry out the light source design of the recognition system， which can provide technical support for the realization of all-weather and high-efficiency intelligent tea bud picking equipment. First， an aluminum alloy frame was constructed to provide a closed and shaded dark environment； then， three combinations of heights and three combinations of light intensities were created by adjusting the height of the crossbar and the brightness of the light source； finally， the image datasets of tea buds in different combinations were collected， and recognition tests were carried out on one bud and one leaf and one bud and two leaves by using the improved YOLOv5 model. The experimental results show that the overall accuracy of YOLOv5s is 77.13%， and the overall average precision mean is 86.14%， the overall accuracy of the improved recognition model YOLOv5s-SPD is 80.30%， and the overall average precision mean is 87.3%， and the average detection time of a single image is 5.7 ms， which meets the requirement of real-time detection， and is better than the original YOLOv5s with an overall accuracy improvement of 3.17% and an overall average precision mean of 1.16%， which effectively improves the recognition performance of tea buds. Under the condition of height 90 cm and luminance L7（0.164~0.328 \mathrm{\mu }\mathrm{m}\mathrm{o}\mathrm{l}/{\mathrm{m}}^{\mathrm{2}}）， the detection accuracy， recall and AP average of one bud and two leaves were 86.70%， 92.45% and 95.00%， respectively. The method proposed in this paper can effectively and quickly detect tea buds， and the light source design scheme supports the development of all-weather high-quality tea intelligent picking equipment.

Seed distribution information detection is an essential prerequisite for ensuring the performance of seeders， providing primary data for precise and efficient management of crop growth processes such as seed quality detection， depth determination and fertilization， field weeding， and harvesting. Computer vision technology has been widely applied in multiple fields by extracting meaningful information from image and video data and analyzing and explaining physical phenomena. Based on a systematic analysis of existing literature， this article explored the research progress of computer vision technology in seed distribution information detection from the perspectives of technology application scenarios， hardware devices， software processing algorithms， etc. The study found that computer vision technology still faces challenges in accuracy and stability in seed distribution information detection， and seed distribution information detection requires real-time and large-scale data processing capabilities. Traditional algorithms and devices are difficult to meet these needs， and data fusion and analysis capabilities still need to be improved. Accurate interpretation of seed distribution information requires comprehensive utilization of multiple sensor data and effective data processing and analysis to obtain more comprehensive and accurate results. In response to the above issues， we proposed the following suggestions： Firstly， the widespread application of deep learning and machine learning algorithms is crucial. By utilizing large-scale annotated data for model training， the accuracy and robustness of the algorithm can be improved； Secondly， it is necessary to improve the adaptability of the algorithm and optimize it for different soil conditions， seed types， and light changes to ensure that the algorithm can work effectively in various environments； At the same time， developing multi-sensor data fusion technology can provide more comprehensive and accurate seed distribution information by fusing image data with other sensor data， such as laser scanning and thermal infrared images. This study aimed to provide a reference for the future development of computer vision in seed distribution information detection.