Published Paper Details:

Mango production in India plays a crucial role in the agricultural sector, but its quality and yield are significantly impacted by various plant diseases. Traditional disease detection methods are time-consuming, costly, and require expertise. This paper proposes a Smart Indian Mango Disease Detection and Classification System utilizing a Convolutional Neural Network (CNN) model for real-time disease identification. The system processes mango leaf images to detect diseases such as anthracnose, powdery mildew, and bacterial black spot, thereby reducing human intervention and ensuring efficient monitoring. The methodology incorporates image pre-processing, feature extraction, classification, and performance evaluation. The experimental results demonstrate an accuracy of 92.4%, proving the system's reliability and effectiveness. Future enhancements may include the integration of IoT-based remote monitoring for real-time disease detection and control.