Potcapsnet: an explainable pyramid dilated capsule network for visualization of blight diseases

Potato tuber is crucial as a primary vegetable food crop across the globe. However, its quality and quantity are constantly threatened by fungal blight diseases, particularly late and early blight, posing a significant risk to food security. To identify blight diseases, agrarians visually inspect potato leaf color variations, which is time-consuming and computationally expensive. To address this challenge, various advanced technologies, such as image processing, machine learning, and deep neural networks, have been widely applied in agricultural domains, especially for autonomous disease identification and classification. However, there is an urgent need to develop computational models that can rapidly and objectively detect these diseases, even in their early stages. Therefore, this paper introduces the “PotCapsNet" method that utilizes parallel atrous convolutional layers with varying dilation rates for multi-scale feature extraction, a shuffled convolutional block attention module for effective feature selection, and a capsule network for early disease classification. The effectiveness of the proposed model was validated using the publicly available PlantVillage and PLD datasets for multi-class potato leaf disease identification. The experimental findings show that the proposed method outperforms other methods in terms of accuracy, specificity, F1 score, and sensitivity. The proposed method attained average recognition accuracy of 97.81 \(\%\) $\%$ and 97.95 \(\%\) $\%$ on the PlantVillage and PLD datasets, respectively, the best among all the considered methods. The code and datasets of the proposed method are available at https://github.com/ersachingupta11/PotCapsNet.