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Author: search.filters.author.Phongcharoenpanich, ChuwongSubject: search.filters.subject.Fruit classification

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    Beam-Scanning Reflectometer for Detecting Granulated Fruits
    (2017-03-01)
    Kittiyanpunya, Chainarong
    ;
    Leekul, Prapan
    ;
    Phongcharoenpanich, Chuwong
    ;
    Krairiksh, Monai
    The problem of granulation in fruits costs vendors a big chunk of their revenue, hence a non-destructive sensor that can identify granulated fruits without destroying good fruits is highly desirable. This paper presents such a sensor system based on beam-scanning reflectometer operating at 2.45 GHz. Beams of scattered waves from several directions around the fruit are reflected by a conical reflector and scanned by a phased array of pattern reconfigurable Yagi-Uda antennas equipped with 1-b phase shifters for lowest loss in feed network. The collected data are used to calculate the Rician k-factor. The system was tested with plasticine spheres and real tangerines. It was found that the system with an eight-beam array was better than one with a four-beam array. This system may prove useful for quality control of tangerines and other agricultural products.
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    Rician k-Factors-Based Sensor for Fruit Classification by Maturity Stage
    (2016-09-01)
    Leekul, Prapan
    ;
    Chivapreecha, Sorawat
    ;
    Phongcharoenpanich, Chuwong
    ;
    Krairiksh, Monai
    This paper has proposed a non-destructive, non-invasive sensor system for classifying durian fruits according to the maturity stages. The design of the sensor system has adopted the principle of wireless communications in which the Rician k-factors were utilized to determine the fruit maturity. The sensor is operable at the 915 MHz frequency. The simulation and experimental results revealed that the export grade durian had the higher reflected wave-based k-factors but lower scattered wave-based k-factors, when compared with the immature durian. The findings also showed that the k-factor thresholds for the reflected and scattered waves of 3 ×10<sup>4</sup> and 128 could achieve an accuracy rate of as high as 92.7% with regard to the durian maturity.