A novel pixel replacement-based segmentation and double feature extraction techniques for efficient classification of plant leaf diseases

Karthickmanoj, R.; Padmapriya, J.; Sasilatha, T.

doi:10.1016/j.matpr.2021.04.416

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…In agricultural applications [25], hydroacoustic signal recognition [26], and even star recognition [27], the double feature extraction method has been well applied, which can strengthen the classification process, combine the different features extracted, and improve the accuracy of classification results. Based on the above reasons, this paper proposes a double feature extraction method combining SlE and FE and applies it to fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

Wang

2022

Mathematical Problems in Engineering

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Rolling bearings are the key components for the safe operation of mechanical equipment. It plays an irreplaceable role in the normal operation of mechanical equipment. Higher load makes higher failure rate of rolling bearing. Accurate identification of the fault location is an important step in the diagnosis of the rolling bearing fault. In recent years, the entropy features of rolling bearing vibration signals are usually extracted to identify fault. In this paper, a double feature extraction method based on slope entropy (SlE) and fuzzy entropy (FE) is proposed to recognize the fault state of rolling bearing through rolling bearing signals. In the single feature extraction experiment, the recognition rate of these two kinds of entropy is not high. Through the improvement of the single feature extraction experiment, SlE and FE are selected as two feature combinations. After combining approximate entropy (AE), SlE, FE, permutation entropy (PE), and sample entropy (SE). The identification rate of these combinations was calculated using k nearest neighbor (KNN). The result shows that the recognition rate of this combination is 98% and 3.3% higher than other combinations.

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Section: Introductionmentioning

confidence: 99%

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

Wang

2022

Mathematical Problems in Engineering

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“…GLCM to extract texture features like energy, homogeneity, and contrast. The leaves are then classified as healthy or diseased using an SVM classifier(Karthickmanoj et al, 2021).T A B L E 3 T A B L E 3 (Continued)…”

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confidence: 99%

An analysis to investigate plant disease identification based on machine learning techniques

Duhan,

Gulia,

Gill

et al. 2024

Expert Systems

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In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

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Computational Intelligence‐Based Optimal Feature Selection Techniques for Detecting Plant Diseases

Karthickmanoj,

Nandhini,

Sasilatha

2024

Computational Intelligence

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A novel pixel replacement-based segmentation and double feature extraction techniques for efficient classification of plant leaf diseases

Cited by 7 publications

References 5 publications

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

An analysis to investigate plant disease identification based on machine learning techniques

Computational Intelligence‐Based Optimal Feature Selection Techniques for Detecting Plant Diseases

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