Fruit classification by HPA-SLFN

Lu, Siyuan; Lu, Zhihai; Phillips, P. Jonathon; Wang, Shuihua; Wu, Jianguo; Zhang, Yudong

doi:10.1109/wcsp.2016.7752639

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…Therefore, Lu [10] proposed a novel "HPA + SLFN" method. At the same time, the method was compared with other five existing methods.…”

Section: Methods and Resultsmentioning

confidence: 99%

A study on classification of fruit type and fruit disease

Yang¹,

Kichida²

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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with the rise of biotechnology, the similarity between fruits is growing, which has some impact on our lives. Hence, it needs to design a system to classify the types of fruits. If the fruit is placed over a certain period of time, it will be infected. Meanwhile, it is also important to classify the disease of fruits. In this paper, we introduce several approaches to classify the type of fruits and fruit diseases automatically. Finally, we make an overall comparison based on these methods.

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“…Therefore, Lu [10] proposed a novel "HPA + SLFN" method. At the same time, the method was compared with other five existing methods.…”

Section: Methods and Resultsmentioning

confidence: 99%

A study on classification of fruit type and fruit disease

Yang¹,

Kichida²

2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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show abstract

“…The accuracy of the proposed approach (89.1%) was higher than that of several other classifiers according to data. Lu et al [6] presented a fruit categorization tool with the primary goal of quickly and precisely identifying fruits. The primary objective of this project was to categorize fruits using computer vision and artificial intelligence.…”

Section: Fruit Identificationmentioning

confidence: 99%

Computer Intelligence-Based Fruit Grading: A Review

Ratha¹,

Barpanda²,

Sethy³

et al. 2023

RIA

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India is the second-largest fruit producer in the world. But, fruit identification, classification, and grading are carried out manually. Hence, most of the harvested fruit was wasted due to human perception subjectivity because there needed to be more qualified workers. Therefore, the fruit sector must impose an automated fruit detection system to distinguish among different types of fruits based on their variety, class, maturity, and quality. An automated system may be created with the use of appropriate image processing ideas and machine learning strategies for grading and quality inspection of fruits. With an emphasis on the advancement of state-of-the-art, this study provides a quick examination of the methodologies put out in the research publications from the last couple of years. Various methods are used to compare the relevant studies.

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“…The iris classification issue is the focus of its application. [15] For fruit classification, a method that combined PSO, ABC, and a single hidden layer feed forward neural network was proposed..…”

Section: Related Workmentioning

confidence: 99%

Networks Data Transfer Classification Based On Neural Networks

Ali

Chalob

Khudhair

2022

WJCMS

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Data transmission classification is an important issue in networks communications, since the data classification process has the ultimate impact in organizing and arranging it according to size and area to prepare it for transmission to minimize the transmission bandwidth and enhancing the bit rate. There are several methods and mechanisms for classifying the transmitted data according to the type of data and to the classification efficiency. One of the most recent classification methods is the classification of artificial neural networks (ANN). It is considered one of the most dynamic and up-to-date research in areas of application. ANN is a branch of artificial intelligence (AI). The neural network is trained by backpropagation algorithm. Various combinations of functions and their effect while utilizing ANN as a file, classifier was studied and the validity of these functions for different types of datasets was analyzed. Back propagation neural university (BPNN) supported with Levenberg Marqurdte (LM) activation function might be utilized with as a successful data classification tool with a suitable set of training and learning functions which operates, when the probability is maximum. Whenever the maximum likelihood method was compared with backpropagation neural network method, the BPNN supported with Levenberg Marqurdte (LM) activation function was further accurate than maximum likelihood method. A high predictive ability against stable and well-functioning BPNN is possible. Multilayer feed-forward neural network algorithm is also used for classification. However BPNN supported with Levenberg Marqurdte (LM) activation function proves to be more effective than other classification algorithms.

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Fruit classification by HPA-SLFN

Cited by 15 publications

References 31 publications

A study on classification of fruit type and fruit disease

A study on classification of fruit type and fruit disease

Computer Intelligence-Based Fruit Grading: A Review

Networks Data Transfer Classification Based On Neural Networks

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