Detection of Apple Defects Based on the FCM-NPGA and a Multivariate Image Analysis

Zhang, Wenzhuo; Hu, Juan; Zhou, Guoxiong; He, Mingyi

doi:10.1109/access.2020.2974262

Cited by 29 publications

(12 citation statements)

References 36 publications

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“…The FCM-FPGA algorithm was used to detect segmented apple defect images, while the normal FCM technique was used to detect the segmented apple defect map. In a multivariate image analysis, the influence of the two techniques on defect detection was also seen [48]. In the above shown table (Table 1), recent research done by various authors related to detection of defects and quality analysis in fruits and vegetables, using various feature extraction algorithms have been presented.…”

Section: Quality Defects Grading and Quantity/identifyingmentioning

confidence: 99%

Trend Review Related To Defects Detection from Fruits and Vegetables

Anjali¹,

Kannan²,

Andrès³

et al. 2021

Preprint

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Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.

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Section: Quality Defects Grading and Quantity/identifyingmentioning

confidence: 99%

Trend Review Related To Defects Detection from Fruits and Vegetables

Anjali¹,

Kannan²,

Andrès³

et al. 2021

Preprint

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show abstract

“…Wu et al [16] proposed an online clustering algorithm by combining FCM algorithm with an online framework set to solve the problem that batch learning cannot deal with large-scale data sets. Zhang et al [17] combined FCM with a nonlinear genetic algorithm and proposed an apple defect detection method to improve fruit defect detection. Shen et al [18] proposed a hyperplane partition method based on FCM to deal with big data clustering.…”

Section: Introductionmentioning

confidence: 99%

An Online Weighted Bayesian Fuzzy Clustering Method for Large Medical Data Sets

Zhang¹,

Xue

2022

Computational Intelligence and Neuroscience

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With the rapid development of artificial intelligence, various medical devices and wearable devices have emerged, enabling people to collect various health data of themselves in hospitals or other places. This has led to a substantial increase in the scale of medical data, and it is impossible to import these data into memory at one time. As a result, the hardware requirements of the computer become higher and the time consumption increases. This paper introduces an online clustering framework, divides the large data set into several small data blocks, processes each data block by weighting clustering, and obtains the cluster center and corresponding weight of each data block. Finally, the final cluster center is obtained by processing these cluster centers and corresponding weights, so as to accelerate clustering processing and reduce memory consumption. Extensive experiments are performed on UCI standard database, real cancer data set, and brain CT image data set. The experimental results show that the proposed method is superior to previous methods in less time consumption and good clustering performance.

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“…Guoxiong Zhou et al, [6] proposed the Fuzzy C-Means and the Non-Linear Programming algorithm in the combination with a multivariate image analysis to detect defective apple fruit. FCM is used in the segmentation part.…”

Section: Introductionmentioning

confidence: 99%

Classification of Fruits using Deep Learning Algorithms

Mirra

Rajakumari

2022

Preprint

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Classification of variety of fruits is an important framework in agriculture field for import and export. Many algorithms were employed for classification of fruits. To remove the noises from the images Gaussian filter is applied during the pre-processing. Bunch of fruits are classified into different classes such as apple, orange and banana. Also their quality is taken into account for preventing health hazards. In this work, the various combinations of fruits are classified into proper variety and after that the quality of the fruit is checked as whether it is defect or non-defect. For the first part, Convolutional Neural Network, AlexNet and MobileNetV2 are employed. MobileNetV2 achieved 100% accuracy for fruit type classification. In the second part, the same kind of fruits are fed into classifier for quality checking. The above said classifiers are used for defect classification also. For defect classification, MobileNetV2 gives 99.89% accuracy for orange and 100% for apple.

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Detection of Apple Defects Based on the FCM-NPGA and a Multivariate Image Analysis

Cited by 29 publications

References 36 publications

Trend Review Related To Defects Detection from Fruits and Vegetables

Trend Review Related To Defects Detection from Fruits and Vegetables

An Online Weighted Bayesian Fuzzy Clustering Method for Large Medical Data Sets

Classification of Fruits using Deep Learning Algorithms

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