Abstract：
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, high-precision, fast, and infield diagnosis of tea stress symptoms in complex natural scenes based on smartphone and deep learning algorithms.
摘要：
多重压力暴露下茶叶生产的主要挑战对其全球市场可持续性产生了负面影响。因此，引入一种内场快速技术来监测茶叶的压力具有巨大的迫切需求。因此,这项研究旨在提出一种基于具有深度学习模型的便携式智能手机检测压力症状的有效方法。首先,开发了一个数据库，其中包含10,000多个复杂自然场景中的茶园树冠图像，其中包括健康(无压力)和三种类型的压力(茶炭疽病(TA)，茶泡枯萎病（TB）和晒伤（SB））。然后，YOLOv5m和YOLOv8m算法适用于区分四种类型的压力症状；其中YOLOv8m算法在识别健康叶子方面取得了更好的性能（98％），TA（92.0%），TB（68.4%）和SB（75.5%）。此外，YOLOv8m算法用于构建TA疾病严重程度的鉴别模型，并取得了满意的结果,中度,严重的TA感染占94%，96%,91%，分别。此外,我们发现YOLOv8m的CNN内核可以有效地提取第2层图像的纹理特征，并且这些特征可以清楚地区分不同类型的压力症状。这对YOLOv8m模型实现四类应激症状的高精度区分做出了巨大贡献。总之,我们的研究提供了一个有效的系统来实现低成本，高精度,快,基于智能手机和深度学习算法的复杂自然场景下茶应激症状的现场诊断。
