Identification of Paddy Leaves Disease using Convolutional Neural Networks

Identification of Paddy Leaves Disease using Convolutional Neural Networks S Muni Ratnam , P Bhavitha , P Asma Khanum , S Madhavi , M Bharathi 1 4 (October-December) The economic development of any country mainly depends on agriculture Rice is a staple crop and a significant food source for a large portion of the worlds population and rice usage is higher in Asia But the rice crop suffers from various diseases that greatly affect yield capacity and have an impact on food security For the effective disease management and crop detection the disease need to be detected early and diagnosis to be done at the earliest By automating the disease detection process the system can enable early intervention and timely management practices leading to improved crop yield and reduced economic losses The proposed system effectively identifies paddy leaf disease in which a median filter is used to reduce the noise a Fuzzy CMeans FCM algorithm were used for segmentation and a Convolutional Neural Network CNN were uses for classification Contrast homogeneity energy entropy and correlation features are extracted from the segmented image for training and testing the CNN classifier The proposed system effectively detects Brown Spots BS Leaf Blasts LB and Bacterial Blight BLB disease The proposed system was implemented by using MATLAB R2021a software and tested with the help of three data sets with respect to performance parameters sensitivity specificity accuracy and precision The maximum achieved accuracy of the proposed system is 9868

2026