International Journal on Science and Technology

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AI-Based Crop Disease Detection and Multilingual Farmer Assistance System

Author(s) G. Amulya, Mrs. R. Saranya Lakshmi
Country India
Abstract Agriculture is backbone of India, nevertheless myriad farmers face problems owing to non-identification of crop diseases early. Moreover, in several cases, the farmers do not get ample guidance at the right time.

In this project, we have developed a system that uses artificial intelligence for detecting crop diseases from images. The user can upload an image of a plant, and the system will identify the disease and give suitable suggestions.
We used a deep learning model called as MobileNetV2 for improving more accuracy.The system even supports multiple languages so that farmers can use it in their own language easily.
The model achieved an accuracy of 94.40% and shows good performance. This system immensely helps farmers by giving quick and simple solutions.
The application is developed as a web-based system using Python and Flask for the backend, and HTML, CSS, and JavaScript for the frontend. The system allows users to upload crop images through a simple interface, after which the image is processed and analyzed by the trained model. Based on the prediction, the system provides detailed information including the disease name, confidence level, treatment suggestions, fertilizer recommendations, and basic weather-related advice. One of the key features of the system is its multi-language support, which includes English, Telugu, Hindi, Tamil, and Kannada. This ensures that farmers from different regions can easily understand and use the system in their preferred language. Additionally, the system is designed to function efficiently even in low internet conditions, making it suitable for rural areas.
The project also includes features such as user authentication, scan history tracking, and a reminder system for farming activities. These features enhance the usability and practicality of the system.
Keywords Artificial Intelligence, Crop Disease Detection, Machine Learning, Deep Learning, Chatbot, Agriculture
Field Computer Applications
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-09
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