A novel light-weight convolutional neural network for rice leaf disease classification

Abstract

Rice is one of the primary sources of staple meals. It may turn out to be a disaster as the production of agricultural products is declined due to diseases and therefore it is required to straighten up the situation by taking precautionary measures. Generally, deep learning (DL) architectures are employed for the identification of plant leaf diseases and it is observed that there is a trade-off between the accuracy and parameters. This study introduces a light-weight architecture called rice leaf disease classification convolutional neural network (RLDC-CNN). The objective of the proposed architecture is to improve the accuracy and reduce the loss by using a combination of convolutional layers, maxpooling layers, and fully connected layers. These layers use activation function for non-linearity, dropout for regularization and implements hyperparameter tuning with various optimizers that include Adam, RMSprop, stochastic gradient descent (SGD) and adaptive gradient (AdaGrad). Experiments are conducted on the dataset of 7,096 images with batch size of 32 under various learning rates. The behavior is analyzed by comparing the existing models and the count of parameters (in millions) equipped by RLDC-CNN, DenseNet121, VGG-16, and ResNet50 is 0.65, 8.49, 15.44, 26.49 with accuracy of 99.15%, 98.94%, 97.82%, 96.48% respectively.