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Rice Plant Infection Recognition using Deep Neural Network Systems

In this work, identification of diseases present in the plant of rice is carried out using methods of Deep Neural Network. So as to achieve image accession, a dataset having 2212 leaf images with different diseases is used. In this work, the entire dataset is divided into two classes in which class 1 contains the healthy leaves and the other class contains infected leaves. The identification is done using VGG-19, LeNet5, and MobileNet-V2predefined Convolutional Neural Network (CNN) models that own a fixed number of Convolutional layers and also the layers that are connected completely also known as fully connected layers. The architecture is designed as per the details for the LeNet5 model while for the other two methods that is MobileNet-V2 and VGG-19, the architecture is directly imported from some predefined libraries which are ready to use, and further, they are used according to the author’s requirement. Once the experiment was completed successfully, it was observed that the accuracy achieved of VGG-19, LeNet5, and MobileNet-V2 was 77.09 %, 76.63 %, and 76.92 %respectively.