Integrating Machine Learning and RAG-Based Chatbot for Mandarin Orange Disease Detection in Region of Nepal
Ritu Raj Lamsal, Prahlad Acharya, Rijan Pokhrel, Sakar Dahal and Vishal Sigdel
ABSTRACT
Citrus farming, particularly Mandarin orange cultivation, is a crucial economic activity in Nepal’s hilly regions. However, disease detection and management remain major challenges. This study presents an efficient method for identifying and controlling five key citrus diseases affecting the Nepali orange market: black spot, canker, Huanglongbing (HLB), leaf miner, and sooty mold. We employ the MobileNetV2 model for disease prediction and a One-Class SVM model for initial leaf classification. Additionally, we integrate a Llama-3.2-11bvision RAG-based chatbot, which analyzes leaf images and provides real-time guidance on disease prevention and orchard management. A mobile application has been developed to integrate the chatbot with a user-friendly interface, making it accessible for farmers. Our approach achieves 95.6% accuracy in disease identification and 85.6% accuracy in orange leaf classification. With its intuitive mobile platform and AI-driven chatbot, this system has the potential to transform citrus farming in Nepal by enabling timely interventions and improved disease management.
