Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks

Yogeshwari, M.; Thailambal, G.

doi:10.1016/j.matpr.2021.03.700

Cited by 56 publications

(35 citation statements)

References 17 publications

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“…Yogeshwari and Thailambal (2021) have offered a new framework based on DCNN for the detection of plant disease. The filtering and enhancement techniques were used in the preprocessing of images.…”

Section: Literature Surveymentioning

confidence: 99%

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Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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Paddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases.Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by "adaptive K-means clustering." This is also accomplished by the Fitness Sorted-Shark Smell Optimization (FS-SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS-SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

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Section: Literature Surveymentioning

confidence: 99%

“…Nevertheless, it has server maintenance problems and not appropriate for individual chats. DCNN (Yogeshwari & Thailambal, 2021) improves the classification accuracy. Still, it suffers from the class imbalance and over fitting.…”

Section: Literature Surveymentioning

confidence: 99%

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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“…One of the most popular and widely used texture-based feature extraction techniques is GLCM (Gray Level Cooccurrence Matrix), which was discovered by Haralick et al (1973) [32].The GLCM feature is a feature that is taken from the relationship between two pixels that are given a certain distance in an image. The GLCM feature has a value that changes rapidly in the fine texture area and a value that changes slowly in the coarse texture area [33] 303 method is used to retrieve the texture properties of an image and represent image information in grayscale [34]. The feature extraction method with GLCM in this study was carried out by taking the values of the texture characteristics, namely: contrast, correlation, energy, and homogeneity, using equations 9, 10, 11, and 12 below [30].…”

Section: Feature Extractionmentioning

confidence: 99%

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Nasution

Alvin²,

Siregar³

et al. 2022

KINETIK

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Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.

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“…Various works have shown that image processing speed and object classification quality increase significantly if the images are subjected to preliminary processing. Image transformation is implemented using filters [41][42][43], thresholding operations [44][45][46], morphological operations [41,47], and artificial neural networks [48,49].…”

mentioning

confidence: 99%

“…M. Yogeshwari et al [44] used the Adaptive Otsu's thresholding algorithm to binarize images of crop leaves. This algorithm made the architecture much easier and accelerated the operation of the convolutional neural network used as a classifier.…”

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confidence: 99%

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

et al. 2021

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The article discusses the problem of detecting sick or mechanically damaged potatoes using machine learning methods. We proposed an algorithm and developed a system for the rapid detection of damaged tubers. The system can be installed on a conveyor belt in a vegetable store, and it consists of a laptop computer and an action camera, synchronized with a flashlight system. The algorithm consists of two phases. The first phase uses the Viola-Jones algorithm, applied to the filtered action camera image, so it aims to detect separate potato tubers on the conveyor belt. The second phase is the application of a method that we choose based on video capturing conditions. To isolate potatoes infected with certain types of diseases (dry rot, for example), we use the Scale Invariant Feature Transform (SIFT)—Support Vector Machine (SVM) method. In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used. Otherwise, Otsu’s threshold binarization—a convolutional neural network (CNN) method is used. The first phase’s result depends on the conveyor’s speed, the density of tubers on the conveyor, and the accuracy of the video system. With the optimal setting, the result reaches 97%. The second phase’s outcome depends on the method and varies from 80% to 97%. When evaluating the performance of the system, it was found that it allows to detect and classify up to 100 tubers in one second, which significantly exceeds the performance of most similar systems.

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Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks

Cited by 56 publications

References 17 publications

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

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