Crop Disease Detection System

Abstract

The Crop Disease Detection System is an innovative approach to identifying and diagnosing plant diseases using Convolutional Neural Networks (CNNs). As crop diseases are a significant threat to global food security, timely detection is essential to prevent yield loss. This system leverages deep learning techniques, particularly CNNs, to analyze images of crops and classify them into various disease categories.
The system is trained on a large dataset of labeled crop images, where the CNN is used to automatically extract relevant features such as texture, color, and shape. The model then classifies the images into healthy or diseased crops, identifying specific diseases like fungal infections, bacterial blights, and viral diseases. By utilizing transfer learning and pre-trained models, the system achieves high accuracy even with limited datasets.
The proposed system offers several advantages, including fast and accurate disease detection, cost-effectiveness, and ease of implementation for farmers in the field. It can be deployed on mobile devices or integrated into precision agriculture systems, providing real-time monitoring and early warnings for farmers to take preventive measures. This system aims to reduce the reliance on manual inspections, lower pesticide usage, and ultimately improve crop productivity and sustainability.