RiceGuardNet: Custom CNNs for Precise Bacterial and Fungal Infection Classification

Rice cultivation is a vital component of many nations’ agricultural landscapes, often relying on traditional knowledge passed down through generations. However, disease identification in rice crops presents challenges, as many diseases are difficult to discern through visual inspection alone. This leads to delayed or inaccurate diagnoses, placing entire plantations at risk and discouraging new entrants to the field. This research addresses the pressing issue of timely and accurate disease identification in rice plants, focusing on three common diseases: Bacterial Leaf Blight, Brown Spot, and Leaf Smut, which are caused by bacteria and fungi. These diseases can proliferate rapidly, making early detection crucial. A custom Convolutional Neural Network (CNN) model was developed and trained using a dataset comprising 16,000 images, with 4,000 images for each disease and a healthy class. The model achieved an impressive accuracy of 99.87% on the test dataset, demonstrating its effectiveness in disease classification. This innovative approach provides a solution to the challenges faced by rice farmers, enabling quick and accurate disease identification. The research findings hold significant promise for improving rice cultivation practices, reducing the risk of crop loss, and encouraging new entrants into the field of rice farming.