Rice leaf Disease Recognition based on deep learning using Zippier S-Method

The rice grain diseases emerge at the last time prior to harvest, the farming process is seriously damaged. However, the rice leaf disease such as blast, leaf blight and so on. The major issue is hispa in rice leaf still yet not detects the disease with high accuracy. Hence, proposed the Zippier S-Convolutional Neural Network to detect the hispa disease based on deep learning. This method automatically predicts the plant diseases and hence it is highly recommended. Also, each image of rice plant is preprocessed to eliminate noise in the image. The data is trained and tested using aggravation method. The proposed method utilizes categorization and regression is carried throughout the proposed method. The proposed method supports the Section Manifesto System to classify the disease and to recognize the rice leaf disease. The Proposed method achieves the accuracy of 99.89% to effectively detect the hispa disease and healthy leaf in rice plant.