Li Zhaowen, Sun Jihong, Shen Yingming, Yang Ying, Wang Xijin, Wang Xinrui, Tian Peng, Qian Ye
College of Big Data, Yunnan Agricultural University, Kunming, 650201, Yunnan, China.
Yunnan Key Laboratory of Crop Production and Smart Agriculture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China.
Plant Methods. 2024 Jul 5;20(1):101. doi: 10.1186/s13007-024-01219-x.
The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche.
Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%.
These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.
茶树病虫害的发生、发展和爆发对茶叶的品质和产量构成了重大挑战，因此需要及时进行识别并采取防治措施。鉴于茶树病虫害种类繁多，再加上茶园种植环境复杂，准确、快速的诊断仍然难以实现。为了解决这个问题，本研究探讨了利用迁移学习卷积神经网络来识别茶树病虫害。我们的目标是在云南大叶种茶树复杂的生态环境中，促进对病虫害的准确、快速检测。
首先，我们从茶园复杂环境中收集了1878幅包含10种常见茶树病虫害类型的图像数据，编制了一个全面的数据集。此外，我们采用数据增强技术来丰富样本多样性。利用ImageNet预训练模型，我们进行了全面评估，确定Xception架构是最有效的模型。值得注意的是，在Xeption模型中集成注意力机制并没有提高识别性能。随后，通过迁移学习和冻结核心策略，我们实现了98.58%的测试准确率和98.2310%的验证准确率。
这些结果标志着在准确、及时检测方面迈出了重要一步，有望提高云南茶叶的可持续性和生产力。我们的研究结果为云南茶树病虫害在线检测技术的发展提供了理论基础和技术指导。
