Anim-Ayeko, Alberta Odamea; Schillaci, Calogero; Lipani, Aldo; (2023) Automatic Blight Disease Detection in Potato (Solanum tuberosum L.) and Tomato (Solanum lycopersicum, L. 1753) Plants using Deep Learning. Smart Agricultural Technology , 4 , Article 100178. 10.1016/j.atech.2023.100178.

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Abstract

Early and late blight are two diseases which pose a huge risk to both potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) crops and make farmers run at a loss. The early and automatic detection of these diseases would save time as well as enable farmers to act quickly on crops which have been affected. Machine learning and deep learning technology provide many solutions for the detection of the blight diseases in affected crops, and are common in the literature. However, explanation methods for such solutions are not common, but are necessary, considering some machine learning models are seen as black boxes. This study proposes a ResNet-9 model which detects the blight disease state of potato and tomato leaf images, which farmers can leverage. With the data obtained from the popular “Plant Village Dataset”, there were 3,990 initial training data samples. After augmenting the training set and a rigorous hyperparameter optimization procedure, the model was trained with these hyperparameter values, and examined on the test set, which contained 1,331 images. A test accuracy of 99.25%, 99.67% overall precision, 99.33% overall recall and 99.33% overall F1-score values were achieved. To fully understand the model, explanations for the proposed model were provided through saliency maps, which showed the reasoning behind the predictions of the model. It was observed that the ResNet-9 model considered the shape of the leaf, diseased areas present and general green areas of the leaf for its predictions and this makes us understand the model predictions better and see that the model behaves as expected. Our results could contribute to the testing and deployment of Convolutional Neural Network (CNN) models for classification of proximal sensing images of potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plant leaves. Further studies would benefit from this modeling framework and would have the chance to test several other variables to determine the leaf infections in an earlier stage for crop protection.

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