Attention augmented residual network for tomato disease detection and classification
Getinet Yilma, Seid Belay, Kumie Gedamu, Maregu Assefa, Melese Ayalew, Ariyo Oluwasanmi, Zhiguang Qin
Abstract
Deep learning techniques help agronomists efficiently identify, analyze, and monitor tomato health. CNN (convolutional neural network) locality constraint and existing small train sample adversely influenced disease recognition performance. To alleviate these challenges, we proposed a discriminative feature learning attention augmented residual (AAR) network. The AAR network contains a stacked pre-activated residual block that learns deep coarse level features with locality context, whereas the attention block captures salient feature sets while maintaining the global relationship in data points, attention features augment the learning of the residual block. We used conditional variational generative adversarial network (CVGAN) image reconstruction network and augmentation techniques to enlarge the training sample size and improve feature distribution. We conducted several experiments to demonstrate the AAR network performance.The AAR network performed 97.04% accuracy without data generation and augmentation, 98.91% with data generation and augmentation, and 99.03% trained with data augmentation, which consistently improved tomato disease recognition and visualization effectiveness in both cases by learning salient features than deep and wide CNN baseline networks and other related works. Therefore, the AAR network can be a good candidate for improved tomato disease detection and classification task.