Papers › Rice grain disease identification using dual phase convolutional neural network based...

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

21 Apr 2020arXiv:2004.09870archive 2025-07-28

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

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%.

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