Date of Publication :4th April 2024

Abstract:This research addresses a pressing concern in the field of rice plant agriculture by developing a CNN model for the detection and recognition of Bacterial leaf blight, Tungro, Brown Spot, Leaf Blast, Leaf Smut. Traditional methods of disease detection are labor-intensive, time-consuming, and require expertise The proposed approach simplifies disease detection while keeping computational demands and resource requirements to a minimum, resulting in a precision rate of 97.93%. This research leverages deep learning, machine learning, and image processing techniques to offer an efficient and accurate solution for the detection of diseases in rice plants. These advancements have the potential to revolutionize the agricultural sector, improve crop health management, and ultimately contribute to enhanced food security.

Reference :