Chatbot-Powered Orchid Leaf Disease Classification and Management for Improved Farming

Abstract

Orchids are important economic plants in Thailand. However, orchid diseases are one of the obstacles affecting the quantity and quality of orchid yields. Classifying orchid leaf diseases requires expertise and might be difficult to follow and manage once discovered. In this research, our objective is to develop an orchid leaf disease classification model and integrate it into the chatbot with management features such as notifying and tracking when a disease is discovered. A dataset was collected from a studying orchid farm in Nakhon Pathom, Thailand, and categorized into two classes: normal orchid leaves and orchid leaves infected with yellow leaf spot disease. Well-known CNN architectures, such as DenseNet201, GoogLeNet, MobileNetV2, etc., are used to develop classification models and measure accuracy. The results show that using MobileNetV2 achieves the highest accuracy of 98%. Subsequently, the orchid leaf disease chatbot system was designed and implemented on the LINE platform.