A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network
01 Jan 2018-Transactions of the ASABE (American Society of Agricultural and Biological Engineers (ASABE))-Vol. 61, Iss: 5, pp 1461-1474
TL;DR: Wang et al. as mentioned in this paper proposed a multichannel convolutional neural network (MCNN), which consists of an input layer, five convolutionsal layers, three subsampling layers and three fully connected layers.
Abstract: . Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance.
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TL;DR: This work reviewed the latest CNN networks pertinent to plant leaf disease classification and summarized DL principles involved in plant disease classification, and summarized the main problems and corresponding solutions of CNN used for plant disease classified.
Abstract: Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.
108 citations
TL;DR: A novel maize leaf disease recognition method based on backbone Alexnet architecture that demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.
Abstract: 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.
101 citations
Cites background from "A Novel Method of Maize Leaf Diseas..."
...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....
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TL;DR: A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight, which outperforms several existing methods in terms of greater precision and frames per second (FPS).
Abstract: 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).
85 citations
Cites methods from "A Novel Method of Maize Leaf Diseas..."
...Convolutional neural network (CNN), a popular method of target detection, has a wide application prospect in the field of crop disease detection [6]–[9]....
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TL;DR: The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification, and outperformed AlexNet and VGG-16.
Abstract: 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 16652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83260 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.
76 citations
Cites background or methods or result from "A Novel Method of Maize Leaf Diseas..."
...Inspired by the design conceptions of parallel networks (e.g., Part-based CNN [8], Two-level Attention CNN [16], MCNN [55], GoogLeNet [72], ResNet [74], and Hypercolumn CNN [90]), we proposed a novel hybrid CNN structure codenamed M-bCNN, which leverages convolutional kernel matrixes to effectively increase the data streams, neurons, and link channels....
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...Furthermore, while shallow-level features can be extracted effortlessly, abstract representations hidden in the deeper level are difficult to obtain without learning procedures [55]....
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...features are often inadequate and lacking in detail [55]....
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..., an augmented image set was constructed through noise addition [84], color jittering [68], PCA jittering [68], rotation blur [55], and scaling blur [85] for their implementation simplicity and satisfactory performances proved in previous researches [23], [52], [72], [73]....
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...Our previous work [55] also suffered these interferences seriously....
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TL;DR: A computer vision approach for plant diseases classification using deep learning convolution neural network, SoyNet, for soybean plant diseases recognition using segmented leaf images that outperforms nine state-of-the-art methods/models.
64 citations