Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks

Capizzi, Giacomo; Sciuto, Grazia Lo; Napoli, Christian; Tramontana, Emiliano; Woźniak, Marcin

doi:10.15439/2015f258

Cited by 53 publications

(31 citation statements)

References 39 publications

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“…Capizzi [16] converted the RGB image to a grayscale image and used the threshold to separate the image from its background. According to the threshold, the grayscale image was converted into a binary image, and the shape feature was extracted with the help of the binary image.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…[29] used co-occurrence matrix and RBPNN with a statistical algorithm on detecting fruit surface defects. In this process, calculating three co-occurrence matrices to extract effective features and using RBPNN to categorize the defect areas.…”

Section: Methodsmentioning

confidence: 99%

Summary on fruit identification methods: A literature review

Fang-yuan¹,

Snetkov²,

Lima³

2017

Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (

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(Purposes) This paper shows the importance and necessity of intelligent identification technology of fruit detection. (Methods) We enumerate several state-of-the-art methods and illustrate the specific application in the process of recognition, by selecting eleven highly related literature. (Results) On this basis, we make an analysis and comparison on the advantages and disadvantages of each approaches. (Conclusion) This summary can be beneficial to researchers who are interested in fruit identification.

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“…Different topologies of Neural Network were experimented in order to gain some insight into the most appropriate network architecture [18], [19], [20], [21].…”

Section: Neural Network Performancementioning

confidence: 99%

Available Bandwidth Estimation in Smart VPN Bonding Technique based on a NARX Neural Network

Capizzi¹,

Sciuto²,

Beritelli³

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-Today many applications require a high Quality of Service (QoS) to the network, especially for real time applications like VoIP services, video/audio conferences, video surveillance, high definition video transmission, etc. Besides, there are many application scenarios for which it is essential to guarantee high QoS in high speed mobility context using an Internet Mobile access. However, internet mobile networks are not designed to support the real-time data traffic due to many factors such as resource sharing, traffic congestion, radio link, coverage, etc., which affect the Quality of Experience (QoE). In order to improve the QoS in mobility scenarios, the authors propose a new technique named "Smart VPN Bonding" which is based on aggregation of two or more internet mobile accesses and is able to provide a higher end-to-end available bandwidth due to an adaptive load balancing algorithm. In this paper, in order to dynamically establish the correct load balancing weights of the smart VPN bonder, a neural network approach to predict the main Key Performance Indicators (KPIs) values in a determinate geographical point is proposed.

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Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks

Cited by 53 publications

References 39 publications

A study on classification of fruit type and fruit disease

A study on classification of fruit type and fruit disease

Summary on fruit identification methods: A literature review

Available Bandwidth Estimation in Smart VPN Bonding Technique based on a NARX Neural Network

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