A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

Lin, Zhongqi; Mu, Shaomin; Shi, Aijun; Pang, Chao; Sun, Xiaoxiao

doi:10.13031/trans.12440

Cited by 39 publications

(22 citation statements)

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“…They were classified into 15 categories based on CafeNet, with the classification precision ranging between 91% and 98%. In addition, some researchers [36], [37] constructed different CNN models to change the ratio between the training set and the testing set, thus improving the accuracy in identifying maize diseases to some extent. Despite the positive results produced by the research above, the increasing sample size and the extended time of training convergence have a negative impact on the accuracy of recognition.…”

Section: Related Workmentioning

confidence: 99%

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Zhou

et al. 2020

IEEE Access

138

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The identification of maize leaf diseases will meet great challenges because of the difficulties in extracting lesion features from the constant-changing environment, uneven illumination reflection of the incident light source and many other factors. In this paper, a novel maize leaf disease recognition method is proposed. In this method, we first designed a maize leaf feature enhancement framework with the capability of enhancing the features of maize under the complex environment. Then a novel neural network is designed based on backbone Alexnet architecture, named DMS-Robust Alexnet. In the DMS-Robust Alexnet, dilated convolution and multi-scale convolution are combined to improve the capability of feature extraction. Batch normalization is performed to prevent network over-fitting while enhancing the robustness of the model. PRelu activation function and Adabound optimizer are employed to improve both convergence and accuracy. In experiments, it is validated from different perspectives that the maize leaf disease feature enhancement algorithm is conducive to improving the capability of the DMS-Robust Alexnet identification. Our method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases. INDEX TERMS Image enhancement, dilated convolution, multi-scale convolution, maize leaf disease, convolutional neural network.

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Section: Related Workmentioning

confidence: 99%

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Zhou

et al. 2020

IEEE Access

138

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“…Although some small targets are detected, there is still a missing detection. The images (4)(5)(6) show the detection effect of model 4, more small diseased position are detected and no missed detection occurred.…”

Section: The Effect Of Transmission Module Combined With Rpn Netwomentioning

confidence: 97%

“…The GIoU can accurately reflect the real detection situation compared with the traditional smooth-L1. The images (1-3) and the images (4)(5)(6) show the detection effects of model 4 and model 6 respectively. Adding GIoU into the basis of original model, the detection accuracy of diseased position is improved.…”

Section: The Effect Of Giou On Mapmentioning

confidence: 99%

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Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

et al. 2020

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Northern maize leaf blight is one of the major diseases that endanger the health of maize. The complex background of the field and different light intensity make the detection of diseases more difficult. A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight. The proposed technique incorporates three major steps of data set preprocessing part, fine-tuning network and detection module. In the first step, the improved retinex is used to process data sets, which successfully solves the problem of poor detection effects caused by high-intensity light. In the second step, the improved RPN is utilized to adjust the anchor box of diseased leaves. The improved RPN network identifies and deletes negative anchors, which reduces the search space of the classifier and provides better initial information for the detection network. In this paper, a transmission module is designed to connect the fine-tuning network with the detection module. On the one hand, the transmission module fuses the features of the low-level and high-level to improve the detection accuracy of small target diseases. On the other hand, the transmission module converts the feature map associated with the fine-tuning network to the detection module, thus realizing the feature sharing between the detection module and the fine-tuning network. In the third step, the detection module takes the optimized anchor as input, focuses on detecting the diseased leaves. By sharing the features of the transmission module, the time-consuming process of using candidate regions layer by layer to detect is eliminated. Therefore, the efficiency of the whole model has reached the efficiency of the one-stage model. In order to further optimize the detection effect of the model, we replace the loss function with generalized intersection over union (GIoU). After 60000 iterations, the highest mean average precision (mAP) reaches 91.83%. The experimental results indicate that the improved model outperforms several existing methods in terms of greater precision and frames per second (FPS). INDEX TERMS Northern maize leaf blight, disease detection, transmission module, retinex, single shot multiBox detector (SSD). I. INTRODUCTION Maize is one of the major food crops in the world. The planting area and output of maize in the world are only lower than that of wheat and rice [1]. In addition to be an excellent feed for animal husbandry, maize is also an important raw material for the development of light industrial products. The associate editor coordinating the review of this manuscript and approving it for publication was Liandong Zhu.

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“…This can severely degrade the accuracy and robustness of the disease classification [53], [54]. Our previous work [55] also suffered these interferences seriously.…”

Section: Introductionmentioning

confidence: 98%

A Unified Matrix-Based Convolutional Neural Network for Fine-Grained Image Classification of Wheat Leaf Diseases

et al. 2019

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Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16 652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83 260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification. INDEX TERMS Convolutional neural network, fine-grained image classification, deep learning, convolutional kernel matrix, wheat leaf diseases.

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A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

Cited by 39 publications

References 0 publications

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

A Unified Matrix-Based Convolutional Neural Network for Fine-Grained Image Classification of Wheat Leaf Diseases

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