Crop Disease Detection Using Convolutional Neural Networks: An Innovative Approach towards Crop Protection
This project aims to leverage the power of Convolutional Neural Networks (CNNs) to develop an automated system for rice disease detection. CNNs have demonstrated remarkable success in various image recognition tasks, making them a promising tool for identifying visual patterns and anomalies. By training a CNN model on a vast dataset of annotated rice disease images, we can empower farmers and agricultural experts with a reliable and efficient tool for early disease detection.
 
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