AgroBot: A Universal AI-Based Agricultural Assistant Integrating CNN Disease Recognition and Multilingual Chatbot Support

Abstract

Plant diseases significantly affect crop productivity, especially in developing regions where timely access to expert agricultural assistance is limited. Recent advancements in deep learning offer promising solutions for automated disease de-tection using plant leaf images. However, existing systems are generally limited by monolingual interfaces, lack of explaina-bility, absence of administrative control, and poor field applicability. This paper presents AgroBot, a universal AI-based agricultural assistant that integrates a custom convolutional neural network (CNN) for image-based plant disease recognition with a multilingual knowledge-driven agricultural chatbot. The system supports 38 crop-disease classes with a training image resolution of 128×128 pixels and additional text-based symp-tom–treatment metadata for eight languages. The CNN model achieved a 95.07% validation accuracy with a validation loss of 0.1969, demonstrating strong classification performance with minimal overfitting. The multilingual CNN-powered chatbot is driven by a structured base of knowledge based on the ideas of a JSON, which allows the domain-consistent agricultural advice. AgroBot also supports a role-based authentication of farmers and administrators, live dataset editing, user management, and a deployment interface that is powered by Streamlit. Findings demonstrate that AgroBot is a viable, effective, and scala-ble solution to AI-assistance in agriculture to enhance the ease and dependability of farmers in rural areas. There are future directions that involve mobile deployment, multilingual chatbot support through speech and the possibility of implementing it with IoT crop monitoring.