Rician k-Factors-Based Sensor for Fruit Classification by Maturity Stage

Leekul, Prapan; Chivapreecha, Sorawat; Phongcharoenpanich, Chuwong; Krairiksh, M.

doi:10.1109/jsen.2016.2581209

Cited by 15 publications

(5 citation statements)

References 4 publications

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“…Leekul et al . [18] has used sensor system in which rician k -factors were utilized to determine the fruit maturity. Durian fruits which were used had high reflection of waves based on k-factor but lower scattered wave k-factor component, when compared with immature durian.…”

Section: Fig 2deshmukh Et Al [22]mentioning

confidence: 99%

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A Research on Fruit Maturity Detection Techniques

2019

IJITEE

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Fruits are the major source of food to humans. Damage in fruits can be of different types. Damage due to insects or damage during transportation are the most common types of damages caused. Fruits are also unfit for human consumption when they mature beyond permissible limit which can be termed as rotten. Fruit maturity detection is divided into different stages depending on type of fruit detection used. Various sensors and image processing methods are used for this purpose. The major stages involved in this process are pre-processing, detection using sensors and image processing. Spectroscopy has been a huge development in image processing. In later stages the results are classified or clustered according to the requirement. This paper presents a survey of the existing fruit maturity detection techniques.

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Section: Fig 2deshmukh Et Al [22]mentioning

confidence: 99%

“…Zawbaa et al [13] Scale invariant feature Transform(SIFT) 9 Lee et al [16] Direct colour mapping 10 Leekul et al [18] Rician Kfactors…”

Section: Fig 4 Ullah Et Al[20]mentioning

confidence: 99%

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A Research on Fruit Maturity Detection Techniques

2019

IJITEE

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“…There are many ways to carry out nondestructive identification or detection of fruit maturity, which are dominated by computer image analysis and processing and sensor-based methods [6][7][8][9][10][11][12] . The two methods are often combined in practical application [13][14] .…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of mature Xanthoceras sorbifolium bunge

Geng

Zhang²,

Wu³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Xanthoceras sorbifolium bunge is a kind of edible oil tree in China, which has very high economic value, but the timely picking of mature fruits is a problem that has troubled farmers for a long time. To rapidly, automatically and accurately identify mature Xanthoceras sorbifolium bunge in the field, a mobile data acquisition and transmission system was firstly designed based on the architecture of the Internet of Things, which provides image acquisition and positioning tools for timely and accurate picking of Xanthoceras sorbifolium bunge. Secondly, a mature Xanthoceras sorbifolium bunge identification network model was constructed based on the lightweight efficient model YOLOv3 by using convolutional neural network (CNN) and flip residual network. The established optimal identification model was evaluated, the results of which indicate that the constructed optimal model can serve as a tool to identify the maturity of Xanthoceras sorbifolium bunge with the mAP of 97.04%.

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“…In smart agricultural technology, non-destructive microwave sensors have been applied for classifying the quality of fruits such as tangerine (Citrus tangerine) [1], durian (Durio zibethinus) [2], and pomelo (Citrus maxima) [3]. These sensor techniques provided a good solution for post harvesting that can sense the quality of fruits off the trees.…”

Section: Introductionmentioning

confidence: 99%

Approximated Backscattered Wave Models of a Lossy Concentric Dielectric Sphere for Fruit Characterization

2022

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Approximated models of electromagnetic waves scattered from a sphere with two different dielectric layers were developed and reported in this paper. We proposed that the dielectric properties of a concentric dielectric sphere object, for example, some types of fruit, could be estimated by this model, from some wave components of the backscattered wave. The models were suitable for lossy objects because only a single bounce of the wave was assumed. In terms of first bounce as well as total backscattered wave results, the reported values agreed well with the values calculated by a commercial software. The measurement results verified the calculated wave components. The dielectric properties determination of real fruits was performed and exhibited the potential in fruit characterization. The main advantage of these models is that they can provide the magnitude and phase information of each backscattered wave component, which makes quality monitoring of fruits to be possible.

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Rician k-Factors-Based Sensor for Fruit Classification by Maturity Stage

Cited by 15 publications

References 4 publications

A Research on Fruit Maturity Detection Techniques

A Research on Fruit Maturity Detection Techniques

Rapid identification of mature Xanthoceras sorbifolium bunge

Approximated Backscattered Wave Models of a Lossy Concentric Dielectric Sphere for Fruit Characterization

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