  Although Convolutional neural networks (CNNs) are widely used for plant
disease detection, they require a large number of training samples when dealing
with wide variety of heterogeneous background. In this work, 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 the 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 architecture. 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 88.07%.
