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

• 全文链接

TLDDM：基于YOLOv8的茶叶病虫害增强检测模型

关键词：

来源：

Agronomy-Basel 期刊

全文链接：

//agri.nais.net.cn/topic/downloadFile/d321c574-6671-433d-990a-64ef01e3d8c4

来源地址：

https://www.mdpi.com/2073-4395/15/3/727

资源所属：

茶学研究专题

类型：

学术文献

语种：

英语

原文发布日期：

2025-03-18

摘要：

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.