As the worlds second most consumed beverage after water, tea is not just a cultural staple but a global economic force of profound scale and influence. More than a mere drink, it represents a quiet negotiation between nature, culture, and the human desire for a moment of reflection. So, the precise identification and detection of tea leaf disease is crucial. With this goal, we have evaluated several Convolutional Neural Networks (CNN) models, among them three shows noticeable performance including DenseNet201, MobileNetV2, InceptionV3 on the teaLeafBD dataset. teaLeafBD dataset contains seven classes, six disease classes and one healthy class, collected under various field conditions reflecting real world challenges. Among the CNN models, DenseNet201 has achieved the highest test accuracy of 99%. In order to enhance the model reliability and interpretability, we have implemented Gradient weighted Class Activation Mapping (Grad CAM), occlusion sensitivity analysis and adversarial training techniques to increase the noise resistance of the model. Finally, we have developed a prototype in order to leverage the models capabilities on real life agriculture. This paper illustrates the deep learning models capabilities to classify the disease in real life tea leaf disease detection and management.

翻译：作为仅次于水的全球第二大消费饮品，茶叶不仅是文化基石，更是具有深远规模与影响力的全球经济力量。它超越单纯的饮品属性，代表着自然、文化与人类对片刻沉思渴望之间的静默平衡。因此，茶叶病害的精准识别与检测至关重要。为此，我们评估了多个卷积神经网络模型，其中DenseNet201、MobileNetV2和InceptionV3在teaLeafBD数据集上展现出显著性能。该数据集包含七类样本（六类病害与一类健康样本），均在反映真实世界挑战的各种田间条件下采集。在卷积神经网络模型中，DenseNet201取得了99%的最高测试准确率。为提升模型可靠性与可解释性，我们引入了梯度加权类激活映射、遮挡敏感性分析及对抗训练技术，以增强模型的抗噪声能力。最终，我们开发了原型系统以在实际农业场景中发挥模型效能。本文阐释了深度学习模型在真实茶叶病害检测与管理中实现病害分类的能力。