A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

Sibiya, Malusi; Sumbwanyambe, Mbuyu

doi:10.3390/agriengineering1010009

Cited by 149 publications

(85 citation statements)

References 7 publications

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“…For further analysis of the performance of DMS-Robust Alexnet, a comparison is performed with GA-SVM [51], SEG-KNN [20], SIMPLE-CNN [33], VGGNet [40], GoogleNet [52] and ResNet [53], maize disease recognition baselines. The comparison results are indicated in Table 7.…”

Section: ) Comparison With Baseline Methodsmentioning

confidence: 99%

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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: ) Comparison With Baseline Methodsmentioning

confidence: 99%

“…The precision of the scheme reached as high as 96.7%. Sibiya et al [33] applied CNN to recognize and classify maize disease images captured by mobile phones. The average recognition precision reached 92.85%.…”

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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“…In this study, classification was carried out to detect diseases in maize leave images using CNN. One of the previous studies that carried out diseases classification of maize leaves using CNN was Sibiya & Sumbwanyambe [20]. They using 3 classes of disease classification: northern leaf blight, common rust, and cercospora.…”

Section: Introductionmentioning

confidence: 99%

“…One hundred images per class was used with a ratio of 70% for training and 30% for testing. The testing results showed an accuracy of 92.85% [20] TELKOMNIKA Telecommun Comput El Control  Convolutional neural network for maize leaf disease image classification (Mohammad Syarief)…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for maize leaf disease image classification

Syarief

Setiawan

2020

TELKOMNIKA

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This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.

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“…In a CNN, relevant contextual features in image categorization problems are automatically discovered, and the use of CNNs is gaining attention [20]. CNNs are being used in relevant agricultural fields such as plant species and disease classification [20][21][22][23], remote sensing [24][25][26], and classification of cattle behavior patterns [27]. Using an image analysis technique to monitor tofu and soymilk may be possible.…”

Section: Introductionmentioning

confidence: 99%

Classification of Soymilk and Tofu with Diffuse Reflection Light Using a Deep Learning Technique

et al. 2019

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Tofu is an ancient soybean product that is produced by heating soymilk containing a coagulation agent. Owing to its benefits to human health, it has become popular all over the world. An important index that determines the final product’s (tofu’s) quality is firmness. Coagulants such as CaSO4 and MgCl2 affect the firmness. With the increasing demand for tofu, a monitoring methodology that ensures high-quality tofu is needed. In our previous paper, an opportunity to monitor changes in the physical properties of soymilk by studying its optical properties during the coagulation process was implied. To ensure this possibility, whether soymilk and tofu can be discriminated via their optical properties should be examined. In this study, a He–Ne laser (Thorlabs Japan Inc., Tokyo, Japan, 2015) with a wavelength of 633 nm was emitted to soymilk and tofu. The images of the scattered light on their surfaces were discriminated using a type of deep learning technique. As a result, the images were classified with an accuracy of about 99%. We adjusted the network architecture and hyperparameters for the learning, and this contributed to the successful classification. The construction of a network that is specific to our task led to the successful classification result. In addition to this monitoring method of the tofu coagulation process, the classification methodology in this study is worth noting for possible use in many relevant agricultural fields.

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A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

Cited by 149 publications

References 7 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

Convolutional neural network for maize leaf disease image classification

Classification of Soymilk and Tofu with Diffuse Reflection Light Using a Deep Learning Technique

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