Attention embedded residual CNN for disease detection in tomato leaves

• Published in: Applied soft computing Vol. 86; p. 105933
• Main Authors: R., Karthik, M., Hariharan, Anand, Sundar, Mathikshara, Priyanka, Johnson, Annie, R., Menaka
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2020
• Subjects: Attention; CNN; Deep learning; Residual connections; Tomato; Deep learning; CNN; Residual connections; Tomato; Attention
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105933

Automation in plant disease detection and diagnosis is one of the challenging research areas that has gained significant attention in the agricultural sector. Traditional disease detection methods rely on extracting handcrafted features from the acquired images to identify the type of infection. Also, the performance of these works solely depends on the nature of the handcrafted features selected. This can be addressed by learning the features automatically with the help of Convolutional Neural Networks (CNN). This research presents two different deep architectures for detecting the type of infection in tomato leaves. The first architecture applies residual learning to learn significant features for classification. The second architecture applies attention mechanism on top of the residual deep network. Experiments were conducted using Plant Village Dataset comprising of three diseases namely early blight, late blight, and leaf mold. The proposed work exploited the features learned by the CNN at various processing hierarchy using the attention mechanism and achieved an overall accuracy of 98% on the validation sets in the 5-fold cross-validation. •An attention based deep residual network is proposed in this research to detect the type of infection in tomato leaves.•This enhanced deep learning architecture is the first of its kind developed for automatic detection of infection in tomato leaves.•95999 images were used for training the model and 24001 images were used for validation purpose.•Experimental results indicate that the proposed attention based residual network was able to detect the type of infection with an accuracy of 98%.