A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks

Abstract

The agriculture sector is no exception to the widespread usage of deep learning tools and techniques. In this paper, an automated detection method on the basis of pre-trained Convolutional Neural Network (CNN) models is proposed to identify and classify paddy crop biotic stresses from the field images. The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely, Inception-V3, VGG-16, ResNet-50, DenseNet-121 and MobileNet-28. Brown spot, hispa, and leaf blast, three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation. The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61% outperforming the other considered CNN models. The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.