Identifying Disease in Crops Using Image Analysis and Convolutional Neural Networks

Crop diseases represent a substantial threat to agricultural productivity and food security, underscoring the importance of early detection for effective intervention. Traditional methods for disease identification predominantly rely on visual inspection, which can be labor-intensive, time-consuming, and susceptible to inaccuracies. To mitigate these challenges, automated systems utilizing image processing techniques and Convolutional Neural Networks (CNNs) have emerged as viable alternatives. This study introduces a CNN-based methodology for the precise and efficient detection of crop diseases. It encompasses preliminary image processing steps, including enhancement, segmentation, and feature extraction, aimed at improving image quality and isolating pertinent areas. The processed images are subsequently input into a CNN model that learns multi-dimensional visual features and categorizes them into distinct disease groups. The resultant model exhibits high accuracy in identifying and classifying various crop diseases, thereby providing a valuable resource for timely and effective disease management in agriculture and give a precise result.