Journal of Zhejiang University SCIENCE  B

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Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases


Author(s):  Jinxian TAO1, Xiaoli LI1, Jingfei ZHANG3, Muhammad SHOAIB1, Muhammad adnan ISLAM1, Ibrar AHMAD1, Yong HE1, Sitan YE1, Yujie WANG1, Binhui LIAO3, Mostafa GOUDA1, 2

Affiliation(s):  1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2Department of Nutrition & Food Science, National Research Centre, Dokki, 12622, Giza, Egypt 3Liandu Agriculture and Rural Bureau, Lishui 323000, China

Corresponding email(s):  Xiaoli LI, xiaolili@zju.edu.cn Jingfei ZHANG, 13857077928@163.com

Key Words:  Tea disease detection; YOLO11n; Convolutional module; Attention mechanism; Loss function.


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Jinxian TAO1, Xiaoli LI1, Jingfei ZHANG3, Muhammad SHOAIB1, Muhammad adnan ISLAM1, Ibrar AHMAD1, Yong HE1, Sitan YE1, Yujie WANG1, Binhui LIAO3, Mostafa GOUDA1,2. Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2500693

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journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.B2500693"
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Abstract: Tea diseases, including brown and grey blight, result in significant yield and quality losses, especially in Longjing tea production. Traditional detection methods are prone to errors, while existing deep learning models often struggle to be robust in natural field conditions. To address these challenges, an improved lightweight detection model, Asymmetric Multi-level mechanism, Dynamic Snake convolution, SIoU loss function-YOLO (ADS-YOLO), was developed and validated. In the method, a dataset comprising 5694 smartphone-captured images of tea leaves was established under natural lighting. Enhancements were implemented in the YOLO11n baseline algorithm through the incorporation of the SIoU loss function for better bounding box regression, dynamic snake convolution (DSC), which realizes adaptive feature extraction based on the dynamic spatial context, and an Asymmetric Multi-level (AML) mechanism, which achieves lightweight feature fusion via adaptive multi-scale design. The results showed that ADS-YOLO achieved a precision of 0.935 and a recall of 0.870, compared to 0.894 and 0.818, respectively, using the baseline YOLO11n. Importantly, ADS-YOLO demonstrated a real-time performance of 137.1 FPS, coupled with reduced computational costs. ADS-YOLO improved mAP@0.5 by 6.4% compared with YOLOv5n and achieved up to 44.6% higher accuracy than YOLOv7t. In conclusion, ADS-YOLO achieved high accuracy, providing a scalable solution for real-time crop health monitoring and sustainable, precision agriculture for tea prodution.

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On-line Access: 2026-03-09

Received: 2025-10-30

Revision Accepted: 2026-01-28

Crosschecked: 0000-00-00

Cited: 0

Clicked: 578

Citations:  Bibtex RefMan EndNote GB/T7714

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