Abstract
Plant leaf diseases are closely related to people’s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective.
Highlights
With the rapid development of computer technology, traditional machine learning methods have been applied in plant diseases prediction more and more widely
This paper proposed an improved structure of convolutional neural networks for the identification
This paper proposed an improved structure of convolutional neural networks for the and classification of a large dataset of different plant leaf diseases
Summary
With the rapid development of computer technology, traditional machine learning methods have been applied in plant diseases prediction more and more widely. With the popularity of machine learning algorithms in computer vision, in order to improve the accuracy and speed of diagnostic results, researchers have studied automated plant disease diagnosis based on traditional machine learning algorithms, such as random forest, k-nearest neighbor and support vector machine (SVM) [1,2,3]. By extracting the color and texture characteristics of grape disease leaves, Tian et al used a support vector machine (SVM). Wang et al developed a discriminant analysis method to identify cucumber lesions, by extracting the color, shape and texture features of leaf lesions, as well as combining with environmental information [6]. Zhang et al extracted the color, shape and texture features of lesion after lesion segmentation, and used them to identify five types of corn leaves by K-nearest neighbor (KNN) classifier [7]
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