Study on the potential of combined GLCM features towards medicinal plant classification

Mukherjee, Gunjan; Chatterjee, Arpitam; Tudu, Bipan

doi:10.1109/ciec.2016.7513746

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…By calculating different directions and window sizes of the GLCM, Pacifici et al [ 14 ] extracted multi-scale texture features from very high-resolution panchromatic imagery. Mukherjee et al [ 15 ] preprocessed and combined the texture features by extracting from the GLCM and employed a BP-MLP (backpropagation-multilayer perceptrons) neural network to classify two types of medicinal plants. Li et al [ 16 ] performed a principal component analysis of the image and employed the GLCM to extract texture features from the first two principal components.…”

Section: Introductionmentioning

confidence: 99%

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Xin

Cui

Wang

et al. 2017

Sensors

177

119

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To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved.

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Section: Introductionmentioning

confidence: 99%

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Xin

Cui

Wang

et al. 2017

Sensors

177

119

View full text Add to dashboard Cite

show abstract

“…the probability that i and j have similar intensity. P is the square array in which the image under examination has the number of rows and columns equals to the number of distinct gray levels of the image (Mukherjee et al 2016) [37]. For a given displacement vector the GLCM matrix consists of eight number of rows and eight number of columns.…”

Section: Gray-level Co-occurrence Matrix (Glcm) Based Feature Extractionmentioning

confidence: 99%

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

Nirmala¹,

Gomathy²

2023

Computer Systems Science and Engineering

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In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network (CNN) is proposed for automatic pumpkin leaf image classification. The Proposed Denoising and deep Convolutional Neural Network (CNN) method enhances the Pumpkin Leaf Preprocessing and diagnosis. Real time data base was used for training and testing of the proposed work. Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the proposed method. The system and computer simulations were performed using Matlab tool.

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“…By ascertaining various bearings and window sizes of the GLCM, Pacific et al [14] removed multi-scale surface highlights from exceptionally high-goal panchromatic symbolism. Mukherjee et al [15] pre-processed and consolidated the surface highlights by extricating from the GLCM and utilized a BP-MLP (backpropagation-multilayer perceptron's) neural organization to order two sorts of therapeutic plants. Li et al [16] played out a foremost segment examination of the picture and utilized the GLCM to extricate surface highlights from the initial two head segments.…”

Section: Related Workmentioning

confidence: 99%

Classification of Leaf Diseases in Paddy Plant Based on Combined Approach of Texture and Colour Feature Extraction and Optimized Feature Selection

Nithiya¹,

Annapurani²

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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This paper proposes a methodology for the classification of leaf diseases by using the different characterization of shape, and colour properties. Paddy plant diseases are discussed in this research work. Bacterial light, Brown Spot, Leaf smut diseases is identified in paddy crops. A plant leaf is pre-processed at first utilizing the improved BAHE (brightness adapted histogram equalization). Leaf image is segmented using K mean clustering algorithm. The feature extraction is improved by combination of texture feature and colour features. The extracted feature is given as an input for feature selection using optimized firefly algorithm's after that it is used for the classification of diseases in paddy plant. The optimization result is compared with PSO and Genetic algorithm..

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Study on the potential of combined GLCM features towards medicinal plant classification

Cited by 11 publications

References 8 publications

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

Classification of Leaf Diseases in Paddy Plant Based on Combined Approach of Texture and Colour Feature Extraction and Optimized Feature Selection

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