用注意力机制和可视化提升茶叶病害识别准确率
Enhancing Tea Leaf Disease Recognition with Attention Mechanisms and Grad-CAM Visualization
- 引入注意力模块与集成模型增强特征提取能力
- 在7类5278张图像上达85.68%最高准确率
- 结合Grad-CAM实现可解释性，适合农业开发者
茶叶是全球消费最广泛的饮品之一，其生产对多国经济至关重要。茶树叶片病害若未能及时控制，将导致农民严重经济损失。人工识别效率低且易出错，自动化识别具有重要意义。本研究构建了一个针对茶叶病害的自动分类系统，旨在帮助农户尽早干预、减少损失。研究开发了包含7个类别共5278张图像的专用数据集，并进行了预处理。实验采用DenseNet、Inception和EfficientNet三种预训练模型，其中EfficientNet仅用于集成模型。通过引入两种注意力机制提升性能，最终集成模型达到85.68%的最高准确率。同时引入可解释AI技术（Grad-CAM）增强模型透明度，便于实际应用中的决策支持。
原文摘要 · Abstract (English)
Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges in tea harvesting is tea leaf diseases. If the spread of tea leaf diseases is not stopped in time, it can lead to massive economic losses for farmers. Therefore, it is crucial to identify tea leaf diseases as soon as possible. Manually identifying tea leaf disease is an ineffective and time-consuming method, without any guarantee of success. Automating this process will improve both the efficiency and the success rate of identifying tea leaf diseases. The purpose of this study is to create an automated system that can classify different kinds of tea leaf diseases, allowing farmers to take action to minimize the damage. A novel dataset was developed specifically for this study. The dataset contains 5278 images across seven classes. The dataset was pre-processed prior to training the model. We deployed three pretrained models: DenseNet, Inception, and EfficientNet. EfficientNet was used only in the ensemble model. We utilized two different attention modules to improve model performance. The ensemble model achieved the highest accuracy of 85.68%. Explainable AI was introduced for better model interpretability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。