AI Based disease detection system
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Date
2026
Journal Title
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Volume Title
Publisher
New Horizon College of Engineering
Abstract
Timely and accurate identification of plant diseases is critical for improving crop yields and ensuring food security. Manual inspection methods are often laborious, time-consuming, and may require specialized agronomic expertise that is not always accessible, particularly to small-scale farmers. This project, titled "AI-BEDS (AI Based Early Diagnosis System)," addresses these limitations by developing an innovative software-based system. The system utilizes deep learning and computer vision techniques to automatically detect and identify various plant leaf diseases directly from digital images. The core of AI-BEDS is a robust Convolutional Neural Network (CNN) model, trained on a comprehensive dataset of plant leaf images. This model is integrated into a user-friendly mobile or web application. This application allows farmers and agricultural workers to easily capture new images of plant leaves using their mobile devices or upload existing images. Upon submission, the system processes the image through the AI model and provides prompt diagnostic feedback. This feedback includes the identified plant species, the name of the detected disease (if any), and a confidence score indicating the reliability of the prediction. A significant aspect of the AI-BEDS project is its design for versatility and accessibility. While the primary interface is through mobile and desktop platforms to ensure broad reach, the system architecture also supports optional deployment on a Raspberry Pi. This feature enables the system to function as an offline, field-deployable diagnostic tool, extending its utility to rural or remote areas where internet connectivity may be unreliable or unavailable. The methodology for this project encompasses several key stages: the collection and meticulous preprocessing of a large and diverse dataset of plant leaf images (e.g., from public repositories like PlantVillage, potentially augmented with custom data); the training and optimization of a suitable deep learning classification model (such as MobileNetV2 or EfficientNet, chosen for their balance of accuracy and efficiency) using frameworks like TensorFlow/Keras; the conversion of the trained model to a lightweight format (e.g., TensorFlow Lite) for efficient deployment on edge devices, including mobile phones and the Raspberry Pi; and the development of an intuitive and responsive user interface for both the mobile/web application and the optional Raspberry Pi setup. Furthermore, the system is designed to include functionality for logging diagnostic results, which can help track disease prevalence over time, and for suggesting general, evidence-based treatment guidelines to assist farmers in making informed decisions. The anticipated outcomes of the AI-BEDS project are a highly accurate, fast, and reliable mobile or web application capable of identifying common plant diseases from leaf images. The optional offline capability provided by Raspberry Pi deployment will further
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enhance its practical value in diverse agricultural settings. From a societal perspective, AI-BEDS aims to empower farmers with an accessible, low-cost tool