Convolutional Neural Networks Based Paddy Leaf Disease Detection

Abstract

Abstract: This study proposes a novel approach for detecting paddy leaf diseases through the integration of Convolutional Neural Networks (CNNs). The process begins by converting input RGB images into HSV color space, where the saturation component is extracted. Subsequently, a binary image is generated, followed by background removal to enhance the accuracy of segmentation. The background-removed image is then converted back to the HSV color space for further processing. Kmeans segmentation is applied to segment the leaf regions effectively. The CNN architecture is employed for classification, categorizing the detected regions into various disease classes such as Healthy, Sheath Rot, Bacterial Leaf Blight, Brown Spot, and Leaf Blast. Additionally, a voice output feature is incorporated to provide audible feedback on the detected diseases. The study focuses on evaluating the accuracy of disease classification, aiming to demonstrate the effectiveness of the CNN approach in improving performance and reducing misclassifications compared to traditional methods. Overall, the proposed methodology offers a promising solution for accurate and efficient paddy leaf disease detection, with potential applications in agricultural management and crop protection