Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks

By Xihai Zhang, Yue Qiao, Fanfeng Meng, Chengguo Fan, Mingming Zhang. IEEE Access, 2018

514 citations recorded by the source corpus.

In the field of agricultural information, the automatic identification and diagnosis of maize leaf diseases is highly desired. To improve the identification accuracy of maize leaf diseases and reduce the number of network parameters, the improved GoogLeNet and Cifar10 models based on deep learning are proposed for leaf disease recognition in this paper. Two improved models that are used to train and test nine kinds of maize leaf images are obtained by adjusting the parameters, changing the pooling combinations, adding dropout operations and rectified linear unit functions, and reducing the number of classifiers. In addition, the number of parameters of the improved models is significantly smaller than that of the VGG and AlexNet structures. During the recognition of eight kinds of maize leaf diseases, the GoogLeNet model achieves a top - 1 average identification accuracy of 98.9%, and the Cifar10 model achieves an average accuracy of 98.8%. The improved methods are possibly improved the accuracy of maize leaf disease, and reduced the convergence iterations, which can effectively improve the model training and recognition efficiency.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/76f83895915025603858736ae356670ca1fbce7f

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Citations in the corpus

Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks (2019, background): “are not rare in crop disease detection currently [17]–[23].”
Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, background): “in [18] for the classification of diseases from the maize leaf images.”
An optimized dense convolutional neural network model for disease recognition and classification in corn leaf (2020, methodology): “An improved GoogLeNet and Cifar 10 model was proposed in [7] for classification of 8 corn leaf diseases (southern leaf blight, brown spot, rust, round spot, dwarf mosiac, curvularia leaf spot, gray leaf spot, northern leaf blight).”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “The customized Data Sets are present in 66 studies (S1, S2, S3, S6, S7, S8, S9, S12, S15, S17, S18, S20, S22, S24, S25, S28, S32, S33, S34, S36 , S37, S38, S40, S43, S44, S45, S46, S47, S49, S50, S52, S58, S64, S68, S69, S70, S71, S73, S74, S75, S77, S79, S80, S82, S84 , S87, S88, S89, S90, S91, S92”
Plant Disease Detection and Classification by Deep Learning (2019, result): “According to some of the research papers, new/modified DL architectures have been introduced to obtain better/transparent detection of plant disease, such as [86] presented improved GoogLeNet and Cifar-10 models and their performance compared with AlexNet and VGG.”
Plant leaf disease classification using EfficientNet deep learning model (2021, unknown): “In addition, it was observed that some other studies not only used PlantVillage dataset but also some other plant images obtained from the real environment (Chen et al., 2020b; Ferentinos, 2018; KC et al., 2019; Khan et al., 2018; Ma et al., 2018; Zhang et al., 2018a).”