Rice plant disease classification prediction

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  Rice plant diseases

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  Resnet50 model architecture

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  Xception model for rice plant diseases

This study provides a comprehensive understanding of rice plant diseases and the deep learning approaches used to detect them. It analyzes various techniques and discovers the most accurate ways to identify rice plant illnesses.This paper presents a survey of different image processing and deep learning techniques used to identify rice plant diseases. It also discusses important concepts of image processing and deep learning techniques applied to plant disease prediction and classification.We are doing a survey of different deep learning techniques like dilated CNN, probabilistic CNN,VGG 16 and GoogleNet VGG19,PreLu and Xception.ResNet 50 is used for classification of different rice plant diseases.

What's next for Rice plant disease classification prediction

Here in this paper we are using different image processing techniques for classification.and finally we are using a Xception deep learning algorithm for prediction of rice plant diseases.Xception deeply predicts the diseases, therefore it has so many applications of different diseases because of its deep learning characteristics.In this paper we are using Xception for rice plant diseases for better prediction output.Rice diseases can be broadly classified into fungal,bacterial and viral disease.fungal diseases,such as brown spot and leaf blast,are caused by fungus.

Built With

• blast
• cnn
• deep-cnn
• resnet
• rpn-algorithm
• shallow-cnn
• vgg-16
• vgg-19
• xception