Dual phase convolutional neural network based system aimed at small rice grain dataset for disease identification

Tashin Ahmed, Chowdhury Rafeed Rahman, Md. Faysal Mahmud Abid

Keywords: Faster RCNN, Dual phase detection, Small dataset, Rice grain, Convolution

TL;DR: A dual phase strategy that can learn from a small dataset on rice grain diseases has been put forth.

Abstract: Although Convolutional Neural Networks (CNNs) are widely used for plant disease detection, they require a large number of training samples while dealing with wide variety of heterogeneous background. In this paper, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from an image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed on such derived and simplified samples using CNN architectures. Comparison of the dual phase approach with straight forward application of CNN on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.11%.