TLDDM: An Enhanced Tea Leaf Pest and Disease Detection Model Based on YOLOv8

The detection and identification of tea leaf diseases and pests play a crucial role in determining the yield and quality of tea. However, the high similarity between different tea leaf diseases and the difficulty of balancing model accuracy and complexity pose significant challenges during the detection process. This study proposes an enhanced Tea Leaf Disease Detection Model (TLDDM), an improved model based on YOLOv8 to tackle the challenges. Initially, the C2f-Faster-EMA module is employed to reduce the number of parameters and model complexity while enhancing image feature extraction capabilities. Furthermore, the Deformable Attention mechanism is integrated to improve the model’s adaptability to spatial transformations and irregular data structures. Moreover, the Slimneck structure is incorporated to reduce the model scale. Finally, a novel detection head structure, termed EfficientPHead, is proposed to maintain detection performance while improving computational efficiency and reducing parameters which leads to inference speed acceleration. Experimental results demonstrate that the TLDDM model achieves an AP of 98.0%, which demonstrates a significant performance enhancement compared to the SSD and Faster R-CNN algorithm. Furthermore, the proposed model is not only of great significance in improving the performance in accuracy, but also can provide remarkable advantages in real-time detection applications with an FPS (frames per second) of 98.2.