Improved Tea Detection Based on Yolov8

With the rapid development of deep learning technology, deep learning-based object detection algorithms have made remarkable progress in the field of image processing, which has the advantages of high accuracy, high robustness and high efficiency. In this study, we are committed to applying deep learning object detection technology to the detection and recognition of young tea leaves. On the basis of synthesizing the research status at home and abroad, and based on the YOLOv8 algorithm, a young tea leaf detection model based on improved YOLOv8 was proposed. In this study, the key aspects of data processing, model design, training process, and experimental setup are described in detail, and experimental simulations and results analysis are carried out to compare the performance of different models and optimize the models. The final results show that the tea young leaf detection algorithm based on the improved YOLOv8 has achieved significant improvement in detail detection and detection accuracy, and shows a high degree of detection accuracy and robustness. The research results are of great significance for tea production and quality control, and are expected to improve the detection efficiency and accuracy of young tea leaves, and contribute to the development of the tea industry.