Application of Lightweight Deep Learning Models for Plant Disease Classification via LINE Chatbot

Abstract

Smart farming is becoming increasingly important in Thailand, especially with advances in artificial intelligence (AI) technology that help reduce reliance on experts and accelerate plant disease diagnosis through leaf images. Therefore, this paper presents a performance comparison of different deep learning models, namely ResNet18, EfficientNet-B0, and MobileNetV3, in classifying cassava diseases using a five-class cassava leaf image dataset and tomato diseases using a ten-class tomato leaf image dataset. Experimental results show that EfficientNet-B0 achieved the highest accuracy and F1-score, followed by MobileNetV3 and ResNet18, respectively. After, EfficientNet-B0 was applied in real-time system, and a prototype was developed by integrating the EfficientNet-B0 model with a LINE chatbot. The system allows users to submit cassava or tomato leaf images and receive automated disease predictions with preliminary treatment recommendations via LINE. The results show that the system performs efficiently and has potential for smart agriculture in the future.