TeaLeafVision: An Explainable and Robust Deep Learning Framework for Tea Leaf Disease Classification

TL;DR

This paper evaluates CNN models for tea leaf disease classification, achieving high accuracy and enhancing interpretability and robustness through techniques like Grad CAM and adversarial training, culminating in a real-world prototype.

Contribution

The study compares multiple CNN architectures on a real-world tea leaf dataset and introduces interpretability and robustness techniques for practical disease detection.

Findings

DenseNet201 achieved 99% accuracy on tea leaf dataset.

Techniques like Grad CAM and adversarial training improved model interpretability and noise resistance.

A prototype was developed for real-life tea leaf disease detection.

Abstract

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.