Abstract :

The AI-Based Crop Disease Detection And Multilingual Voice-Assisted Farming System Is Developed To Address Challenges Faced By Farmers In Identifying Crop Diseases And Accessing Timely Agricultural Guidance. Limited Expert Availability, Language Barriers, And Lack Of User-friendly Digital Tools Often Delay Diagnosis And Lead To Crop Losses. The Proposed System Provides An Accessible And Intelligent Platform That Enables Early Disease Detection And Advisory Support Through Image-based Analysis And Conversational Interaction. The System Utilizes A Lightweight Deep Learning Architecture, MobileNetV2, To Automatically Detect Crop Diseases From Leaf Images And Generate Advisory Recommendations Using A Hybrid Knowledge-driven Approach. A Multilingual Voice Interface Integrated With Speech-to-text And Conversational Processing Allows Farmers To Interact With The System In Telugu, Hindi, And English, Improving Usability And Inclusiveness. Additionally, Contextual Weather Information Is Incorporated To Enhance Advisory Relevance And Support Better Crop Management Decisions. The Application Is Implemented Using Streamlit, Enabling Seamless Integration Of AI Models, Advisory Modules, And User Interaction Within A Unified Environment. Overall, The System Offers A Practical And Scalable Solution That Promotes Early Disease Identification, Informed Decision-making, And Improved Agricultural Productivity Through Accessible AIdriven Support.

Published:

23-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

164-169

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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