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ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 |
doi: 10.3389/fpls.2024.1459292
Tea leaf disease and insect identification based on improved MobileNetV3
Provisionally accepted
Yang Li
1
Yuheng Lu
2*
Haoyang Liu
1*
Jiahe Bai
3*
Chen Yang
2*
Haiyan Yuan
3*
Xin Li
1
Qiang Xiao
1*- 1 Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
- 2 Hangzhou Ruikun Technology Co., Ltd., Hangzhou, China
- 3 Tea station of Xinchang county, Shaoxing, Zhejiang Province, China
Accurate detection of tea leaf diseases and insects is crucial for their scientific and effective prevention and control, essential for ensuring tea leaf quality and yield. Traditional methods for identifying tea leaf diseases and insects primarily rely on professional technicians, which are difficult to apply in various scenarios. This study proposes a recognition method for tea leaf diseases and insects based on improved MobileNetV3. Initially, a dataset containing images of 17 different types of tea leaf diseases and insects was curated, with data augmentation techniques utilized to broaden recognition scenarios. Subsequently, the MobileNetV3 network structure was enhanced by integrating the CA (coordinate attention) module to improve the perception of location information. Moreover, a fine-tuning transfer learning strategy was employed to optimize model training and accelerate convergence. Experimental results on the constructed dataset of tea tree leaf diseases and insects reveal that the initial recognition accuracy of MobileNetV3 is 94.45%, with an F1-score of 94.12%. Without transfer learning, the recognition accuracy of MobileNetV3-CA reaches 94.58%, while with transfer learning, it reaches 95.88%. Through comparative experiments, this study compares the improved algorithm with the original MobileNetV3 model and other classical image classification models (ResNet18, AlexNet, VGG16, SqueezeNet, and ShuffleNetV2). The findings show that MobileNetV3-CA based on transfer learning achieves higher accuracy in identifying tea leaf diseases and insects. Finally, a tea diseases and insects identification application was developed based on this model. The model showed strong robustness and could provide a reliable reference for intelligent diagnosis of tea diseases and insects.
Keywords: tea leaf diseases and insects, Convolution Neural Network, Transfer Learning, MobileNetV3, Recognition and classification
Received: 04 Jul 2024; Accepted: 10 Sep 2024.
Copyright: © 2024 Li, Lu, Liu, Bai, Yang, Yuan, Li and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuheng Lu, Hangzhou Ruikun Technology Co., Ltd., Hangzhou, China
Haoyang Liu, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
Jiahe Bai, Tea station of Xinchang county, Shaoxing, Zhejiang Province, China
Chen Yang, Hangzhou Ruikun Technology Co., Ltd., Hangzhou, China
Haiyan Yuan, Tea station of Xinchang county, Shaoxing, Zhejiang Province, China
Qiang Xiao, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
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