Chilli Leaf Disease Identification and Categorization using Machine Vision

Chilli leaf disease identification is an important task in agriculture to ensure healthy crop production and reduce yield loss. This project presents a machine vision–based system for automatic identification and categorization of chilli leaf diseases using a Convolutional Neural Network (CNN) algorithm. The system is built using a Raspberry Pi as the main controller, integrated with a web camera for capturing real-time images of chilli leaves. The captured images are processed and analyzed using the trained CNN model to detect the presence of diseases. An LCD display is used to show the status and results of the detection process, while a buzzer provides an alert when an abnormal or diseased condition is identified. Additionally, when a diseased leaf is detected, the captured image along with the classification result is automatically sent to a registered email, enabling remote monitoring and timely action. The proposed system offers a cost-effective, efficient, and real-time solution for farmers to monitor crop health, enabling early detection, quick notification, and reducing the dependency on manual inspection.