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Although Convolutional neural networks (CNNs) are widely used for plantdisease detection, they require a large number of training samples when dealingwith wide variety of heterogeneous background. In this work, a CNN based dualphase method has been proposed which can work effectively on small rice graindisease dataset with heterogeneity. At the first phase, Faster RCNN method isapplied for cropping out the significant portion (rice grain) from the image.This initial phase results in a secondary dataset of rice grains devoid ofheterogeneous background. Disease classification is performed on such derivedand simplified samples using CNN architecture. Comparison of the dual phaseapproach with straight forward application of CNN on the small grain datasetshows the effectiveness of the proposed method which provides a 5 fold crossvalidation accuracy of 88.07%.

Rice grain disease identification using dual phase convolutional neural network based system aimed at small dataset