Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion

Adeel, Alishba; Khan, Muhammad Attique; Sharif, Muhammad; Azam, Faisal; Shah, Jamal Hussain; Umer, Tariq; Wan, Shaohua

doi:10.1016/j.suscom.2019.08.002

Cited by 72 publications

(40 citation statements)

References 30 publications

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“…Besides, the fusion of features from different instruments may cause the issue of redundant information. To detect grape leaf disease, Adeel et al (2019) implemented canonical correlation analysis for feature fusion and further performed neighborhood correlation analysis to reduce the dimensionalities and redundant information of the fused data before feeding the fused data into the classifier. This strategy helped to achieve an accuracy of 94.1% that was superior to the existing methods.…”

Section: Discussionmentioning

confidence: 99%

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Liu

Zhu

et al. 2020

Front. Plant Sci.

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Section: Discussionmentioning

confidence: 99%

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Liu

Zhu

et al. 2020

Front. Plant Sci.

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“…The One-vs.-All M-SVM classifier was used as a base classifier and achieved a classification result of 98.10%. Similarly, in [28] , the authors developed an automated system for disease detection in grapes. A number of different features namely, texture, color and geometric were extracted and fused by using the canonical correlation analysis (CCA).Furthermore, the feature optimization was performed by Neighborhood Component Analysis (NCA) and acquired 92% classification accuracy by using an M-class SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Machine vision approach for classification of citrus leaves using fused features

Qadri

Husnain

et al. 2019

International Journal of Food Properties

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The objective of this study was to observe the potential of machine vision (MV) approach for the classification of eight citrus varieties. The leaf images of eight citrus varieties that were grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri. These were acquired by a digital camera in an open environment without any complex laboratory setup. The acquired digital images dataset was transformed into the multifeature dataset that was the combination of binary, histogram, texture, spectral, rotational, scalability and translational (RST) invariant features. For each citrus leaf image, total 57 multi-features were acquired on every non-overlapping region of interest (ROI), i.e. (32x32), (64x64), (128x128), and (256x256). Furthermore, the optimized 15 features using the supervised correlation-based feature selection (CFS) technique were acquired. The optimized multi-features dataset to different MV classifiers namely Multilayer Perceptron (MLP), Random Forest (RF), J48 and Naïve Bayes using10-fold cross-validation method were plugged-in. The results produced by MLP presented an average overall accuracy of 98.14% on ROIs (256x256) outperforming the other classifiers. The classification accuracy values by MLP on the eight citrus leaf varieties,

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“…From the past few years, features extractions gain much attention in the area of pattern recognition based on several applications such as medical (Rehman et al, 2020), surveillance (M. Sharif, Akram, Raza, Saba, & Rehman, 2020a), agriculture (Adeel et al, 2019; Safdar et al, 2019), and many more (M. A. Khan, Javed, Sharif, Saba, & Rehman, 2019b; S. A. Khan et al, 2019). The main purpose of feature extraction is to analyze the patterns into a relevant category based on a few strong points.…”

Section: Proposed Methodologymentioning

confidence: 99%

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

Khan

Qasim

Lodhi

et al. 2020

Microscopy Res & Technique

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In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time‐consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best‐selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

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Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion

Cited by 72 publications

References 30 publications

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Machine vision approach for classification of citrus leaves using fused features

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

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