N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease

Abstract

Powdery wheat (PW) is one of the most common wheat diseases in northern India. It is the most damaging wheat disease and it is prevalent in April to May season. Several methods of machine learning (ML) and Deep Learning (DL) methods are used to do wheat disease classification. The previous DL techniques have not achieved higher accuracy during PW wheat disease classification. In the current study, 450 wheat images are collected from primary and secondary sources. The normalization technique is used for preprocessing. These normalized preprocessed images are input to CNN. The normalized images increase the training and testing accuracy of CNN. Then, this pre-trained model is applied to the CIAGR images dataset via transfer learning method. During testing with images, CNN achieves 89.9% classification accuracy for PW wheat disease. After these pre-trained model is applied to CIAGR dataset images and achieves 86.5% classification accuracy. Moreover, the result shows that pre-trained NCNN model achieves higher accuracy during transfer learning.