Abstract:
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.
