用深度学习提升茶叶病害识别准确率，结合可解释AI与对抗训练增强模型鲁棒性。
Toward Reliable Tea Leaf Disease Diagnosis Using Deep Learning Model: Enhancing Robustness With Explainable AI and Adversarial Training
- 采用EfficientNetB3与DenseNet201，结合对抗训练提升模型抗干扰能力
- 在5278张图像上实现93%准确率，有效区分7类病害及健康叶片
- 通过Grad-CAM可视化定位病变区域，适合农业专家与基层农户使用
茶叶是孟加拉国重要的经济资产，其种植对经济发展至关重要。但茶树易受多种叶部病害侵袭，导致产量下降和品质降低。人工检测耗时且易出错。为此，本研究基于teaLeafBD数据集（含5,278张高分辨率图像，分7类：6种病害+健康叶），构建自动化深度学习诊断模型。采用DenseNet201与EfficientNetB3进行分类，结合数据预处理、增强、分割、对抗训练及可解释AI策略。对抗训练提升模型在噪声输入下的鲁棒性；Grad-CAM用于可视化关键判别区域。实验结果表明，EfficientNetB3达到93%准确率，DenseNet201达91%。该方法能精准识别病害，为现代农业管理提供实用解决方案。
原文摘要 · Abstract (English)
Tea is a valuable asset for the economy of Bangladesh. So, tea cultivation plays an important role to boost the economy. These valuable plants are vulnerable to various kinds of leaf infections which may cause less production and low quality. It is not so easy to detect these diseases manually. It may take time and there could be some errors in the detection.Therefore, the purpose of the study is to develop an automated deep learning model for tea leaf disease classification based on the teaLeafBD dataset so that anyone can detect the diseases more easily and efficiently. There are 5,278 high-resolution images in this dataset. The images are classified into seven categories. Six of them represents various diseases and the rest one represents healthy leaves. The proposed pipeline contains data preprocessing, data splitting, adversarial training, augmentation, model training, evaluation, and comprehension made possible with Explainable AI strategies. DenseNet201 and EfficientNetB3 were employed to perform the classification task. To prepare the model more robustly, we applied adversarial training so it can operate effectively even with noisy or disturbed inputs. In addition, Grad-CAM visualization was executed to analyze the model's predictions by identifying the most influential regions of each image. Our experimental outcomes revealed that EfficientNetB3 achieved the highest classification accuracy of 93%, while DenseNet201 reached 91%. The outcomes prove that the effectiveness of the proposed approach can accurately detect tea leaf diseases and provide a practical solution for advanced agricultural management.
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