Deep Migration Learning-based Recognition of Diseases and Insect Pests in Yunnan Tea under Complex Environments

Abstract

Background: 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 sun-dried green tea within its complex ecological niche. Results 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 99.17% and a verification accuracy rate of 96.3889%. Conclusions 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.

Plant phenotyping relevance

茶葉の病害を画像から認識・検出するCNN手法の開発と評価が研究の中心であり、植物の病害状態を直接推定するため対象範囲に含める。害虫認識も含むが、病害フェノタイピング手法として技術的評価が明確である。

Code and data availability

The paper describes an author-collected dataset of 1878 tea disease/pest images and transfer-learning models, but no block contains any data or code availability statement, public deposit, repository, or URL for the dataset, images, or trained models. The only URLs present are citations to prior work and the paper'sown

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