Yazar "Guyer, Daniel E." seçeneğine göre listele

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    Apple grading using fuzzy logic
    (TUBITAK, 2003) Kavdir, Ismail; Guyer, Daniel E.
    Classification is vital for the evaluation of agricultural produce. However, the high costs, subjectivity, tediousness and inconsistency associated with manual sorting have been forcing the post harvest industry to apply automation in sorting operations. Fuzzy logic (FL) was applied as a decision making support to grade apples in this study. Quality features such as the color, size and defects of apples were measured through different equipment. The same set of apples was graded by both a human expert and a FL system designed for this purpose. Grading results obtained from FL showed 89% general agreement with the results from the human expert, providing good flexibility in reflecting the expert's expectations and grading standards into the results. This application of apple grading can be fully automated by measuring the required features by means of high-tech sensors or machine vision and making the grading decision using FL.
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    Development and applicability of an agarose-based tart cherry phantom for computer tomography imaging
    (Springer, 2015) Donis-Gonzalez, Irwin R.; Guyer, Daniel E.; Kavdir, Ismail; Shahriari, Dena; Pease, Anthony
    Computer tomography (CT) imaging is an effective method for in vivo characterization of object internal attributes including fresh agro-food product quality. Limitations to move CT technology forward into the development of an inline system include the lack of standardized tools (phantoms) for image quality analysis, cross-sharing, and consistent evaluation. The objective of this study was to develop a set of agarose phantoms suitable for detection of pit and pit fragments using CT imaging. Efficiently sorting out these undesirable features during handling and processing will be extremely beneficial to the tart cherry industry. These phantoms can be used on several CT devices (including ultra-fast CT systems) to quantify CT performance, reproducibility, and applicability. This article describes how the phantoms were created, using agarose, a broadly available and inexpensive material. Developed phantoms allow for the measurement of CT image parameters that are relevant to detect fresh cherry pits and/or pit fragments and helps in the development of inline CT equipment. Measured phantom CT image parameters include simulated flesh and embedded pit X-ray CT attenuation properties (HU-values), which are statistically similar (p = 0.05) to fresh tart cherries. In addition, using CT images, pit and pit fragment size can be inferred with a high accuracy rate (R = 0.99, p value <0.01).