Abstract
Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of plants by themselves when recognizing diseases. In this article, a variety of machine learning methods are used to classify and detect leaf blight. We use the fusion of deep convolutional neural network (CNN) models obtained from SqueezeNet and ShuffleNet to improve the accuracy and robustness of large-scale applications. We use entropy to reduce the complexity of the calculation process and reduce the features in the deep learning process. In addition, we use a support vector machine (SVM) classifier to obtain the classification. We use the CIELAB color space to capture the entire color range to improve accuracy. Our results are very promising because we have achieved 98% accuracy in the early detection of leaf blight.