Attention embedded residual CNN for disease detection in tomato leaves
By K. R., H. M, S. Anand, Priyanka Mathikshara, Annie Johnson, M. R. Applied Soft Computing, 2020
504 citations recorded by the source corpus.
Abstract 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.
Subfields: Plant Science
View Paper: https://www.semanticscholar.org/paper/908d5b3385c0f6463ba7b6b8662fac9bd16a0708
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Citations in the corpus
- Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network (2020, background): “The hidden layers usually consist of convolutional layers, ReLU layers, pooling layers, and fully connected layers [35,36].”
- An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “Numerous research, such as (Karthik et al., 2020) have demonstrated the efficacy of the attention based prediction system.”