Integrating Machine Learning and RAG-Based Chatbot for Mandarin Orange Disease Detection in Hilly Region of Nepal

Abstract

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-3.2-11b-vision 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.

Plant phenotyping relevance

葉画像から柑橘病害を識別する手法と、その精度評価を中心に扱うため、植物の病害状態を推定するフェノタイピング手法として収録対象。

Code and data availability

The paper describes a MobileNetV2/One-Class SVM citrus disease detection system and a RAG chatbot, but provides no authors' public code, trained model checkpoints, or paper-specific dataset deposit. The only dataset mentioned is the publicly available Mendeley dataset by Rauf et al. (ref 15), which is cited prior work,

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