Rice Leaf Diseases Recognition Based on Deep Learning and Hyperparameters Customization

Rice disease prediction plays important task for automated rice disease recognition systems. Feature extraction and classification based on deep learning play important tasks in vision-based diseases recognition. The advancement of deep convolutional neural network using mage data illustrates the approach for identification of rice diseases using deep features with the expectation of high returns. Instead of fine-tuning task which concerns estimation of internal parameters of a model to adjust precisely with certain observations. This paper focuses on extrinsic parameters for model training, which utilities for improving precise of recognition system. Some pretrain models AlexNet, ResNet101 were implemented as mainstream of the convolutional neural network (CNN) architecture. Our approach directly estimates locations of features based on deep learning classification for of rice leaf diseases recognition. There are four kinds of the rice diseases investigated, such as rice blast, bacterial leaf blight, alum poisoning, and leaf folder. A large dataset resolution images from real scenes in the farm were collected for training and evaluation. In this study, the augmentation of image also applied for evaluation input images. The output prediction results of set samples are used for voting final decision. The experimental results show that the proposed approach with hyperparameters customization and data augmentation outperforms.