Classifying Early Blight and Late Blight of Potato Based on Convolution Neural Network

Abstract

HighlightsA convolution neural network (CNN) model, called M_Net, was designed to recognize early blight and late blight in potato.Our model achieves the highest accuracy with low computation requirements compare with other popular deep neural networks.Hyperparameters tuning is performed to optimize accuracy, generalization, and computation requirements for potato disease classification.Experimental results show that the combination of multiple datasets improves the generalization of the model.Abstract.Early and late blight are two of the most common potato diseases. Intelligent tools for automatically detecting these two diseases can benefit farmers and agricultural extension officers. However, it remains a challenge to use traditional image processing methods to recognize these diseases. Convolution neural network (CNN) is an advanced methodology in computer vision, which shows great promise in image classification. This article explores CNN models to classify potato early blight and late blight based on leaf images. This research task has three challenges: lack of adequate datasets, noise in existing data, and the construction of a model that handles variability in image backgrounds. This research designs a CNN model M_Net based on MobileNetV1 network and uses different dataset sources in the construction of a CNN model with a strong generalization ability to identify disease leaves and healthy leaves. Furthermore, this article adds a new dataset to the field by supplying the model with potato leaf images. The results show that the CNN model achieves the highest accuracy with low calculation cost compared to some classical models and the final model has a strong generalization capacity. Keywords: Accuracy, CNN, Early blight, Generalization ability, Late blight.