Identify Apple Leaf Diseases Using Deep Learning Algorithm
This work addresses agricultural production losses due to pests and diseases for farmers, but it is incremental as it uses an existing method on a specific dataset.
The paper tackled the problem of identifying apple leaf diseases by applying a CNN-based classification model to a dataset of 3,642 images, achieving an accuracy of 93.765%.
Agriculture is an essential industry in the both society and economy of a country. However, the pests and diseases cause a great amount of reduction in agricultural production while there is not sufficient guidance for farmers to avoid this disaster. To address this problem, we apply CNNs to plant disease recognition by building a classification model. Within the dataset of 3,642 images of apple leaves, We use a pre-trained image classification model Restnet34 based on a Convolutional neural network (CNN) with the Fastai framework in order to save the training time. Overall, the accuracy of classification is 93.765%.