Plant disease detection and diagnosis
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
The "Plant Disease Detection and Diagnosis using Deep Learning" project is designed to provide an innovative and effective solution to ansist farmers in identifying and diagnosing plant diseases in real-time. The system leverages TensorFlow Lite, a lightweight framework optimized for mobile and edge devices, to deploy a deep learning model trained on a dataset of 1.5k images. These images represent seven plant disease classes, including rust, mold, blight, rot, powdery mildew, scab, and spot. By using computer vision techniques combined with Mediapipe for real-time detection, the system can analyze plant images captured from mobile devices or cameras, offering instant feedback to farmers regarding the presence of disease.
The deep learning model has been specifically designed to run efficiently on resource-constrained devices, ensuring that farmers can easily deploy the solution in the field. The backend of the system is powered by Flask, while the frontend imerface is built using HTML, CSS, and JavaScript to provide a simple, user-friendly platform. The web interface allows farmers to upload images of their plants, where the system analyzes the data, diagnoses the disease, and recommends appropriate remedies. This real-time diagno sis helps farmers take immediate corrective actions to prevent the spread of diseases, reducing potential crop losses and improving productivity.
In addition to diagnosis, one of the key features of this system is its ability to support multiple languages. Using the Google Translate API, the system can translate disease diagnosis and remedial advice into several regional languages, including Kannada, Hindi, Malayalam, Telugu, and Tamil. This multilingual feature ensures that the system is accessible to farmers in different linguistic regions, empowering them to understand the diagnosis and suggested remedies in a language they are familiar with, thereby bridging communication gaps in agriculture.