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Advanced deep learning algorithm for instant discriminating of tea leave stress symptoms by smartphone-based detection

文献类型： 外文期刊

作者： Huang, Zhenxiong 1 ; Gouda, Mostafa 1 ; Ye, Sitan 1 ; Zhang, Xuechen 4 ; Li, Siyi 2 ; Wang, Tiancheng 1 ; Zhang, Jin 5 ; Song, Xinbei 1 ; Li, Xiaoli 1 ; He, Yong 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, 866 Yuhangtang Rd, Hangzhou 310058, Peoples R China

2.Fujian Agr & Forestry Univ, Coll Mech & Elect Engn, 63 Xiyuangong Rd, Fuzhou 350100, Peoples R China

3.Natl Res Ctr, Dept Nutr & Food Sci, Giza 12622, Egypt

4.Anhui Agr Univ, Coll Engn, Changjiangxi Rd, Hefei 230036, Peoples R China

5.Chinese Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Zhejiang, Peoples R China

关键词： Tea leaves; Infield stress detection; Canopy image; Deep learning models; YOLOv8m algorithm; Natural scenes

期刊名称：PLANT PHYSIOLOGY AND BIOCHEMISTRY （ 影响因子：6.1； 五年影响因子：6.2 ）

ISSN： 0981-9428

年卷期： 2024 年 212 卷

页码：

收录情况： SCI

摘要： The primary challenges in tea production under multiple stress exposures have negatively affected its global market sustainability, so introducing an infield fast technique for monitoring tea leaves' stresses has tremendous urgent needs. Therefore, this study aimed to propose an efficient method for the detection of stress symptoms based on a portable smartphone with deep learning models. Firstly, a database containing over 10,000 images of tea garden canopies in complex natural scenes was developed, which included healthy (no stress) and three types of stress (tea anthracnose (TA), tea blister blight (TB) and sunburn (SB)). Then, YOLOv5m and YOLOv8m algorithms were adapted to discriminate the four types of stress symptoms; where the YOLOv8m algorithm achieved better performance in the identification of healthy leaves (98%), TA (92.0%), TB (68.4%) and SB (75.5%). Furthermore, the YOLOv8m algorithm was used to construct a model for differentiation of disease severity of TA, and a satisfactory result was obtained with the accuracy of mild, moderate, and severe TA infections were 94%, 96%, and 91%, respectively. Besides, we found that CNN kernels of YOLOv8m could efficiently extract the texture characteristics of the images at layer 2, and these characteristics can clearly distinguish different types of stress symptoms. This makes great contributions to the YOLOv8m model to achieve high-precision differentiation of four types of stress symptoms. In conclusion, our study provided an effective system to achieve low-cost, highprecision, fast, and infield diagnosis of tea stress symptoms in complex natural scenes based on smartphone and deep learning algorithms.

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