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Non-destructive Control of Fruit Quality via Millimeter Waves and Classification Techniques

Abstract : Fast and efficient non-Destructive evaluation methods for food control is still an ongoing field of research. We have recently proposed to combine W-band imaging with nonlinear SVM classifier to sort out healthy from damaged fruits for a single variety of fruits. We have tested it on apples and peaches separately with a mean accuracy of 96%. We have also shown the limitation of a bi-class SVM since it has failed to sort healthy from damaged fruits when the set of fruits was composed of a mix of apples and peaches. In this paper, we continue to explore the capability of SVM associated with mmW and lowTHz measurements. Firstly, we tackle the problem of classifying a mix of fruits with a multi-class SVM using the Digital Binary Tree architecture. With this method, the error rate does not exceed 2%. Secondly, we move from W- to D-band (low-THz). The main reason is the increase of the lateral resolution and the possibility to have more compact systems in the view of an industrial deployment. We start our D-band investigations with range measurements to estimate the average permittivity of the apple in this frequency bandwidth. We have found a drastic decrease compared to the microwave region. It is consistent with the behavior of the water, which is one of the main components of the apple. Then we have trained the SVM with the D-band database and finally performed the classification on unknown samples and obtained an accuracy of 100%.
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Contributor : sophie gaffé-clément Connect in order to contact the contributor
Submitted on : Monday, August 31, 2020 - 2:32:20 PM
Last modification on : Thursday, August 4, 2022 - 4:56:56 PM



Flora Zidane, Jérôme Lanteri, Julien Marot, Laurent Brochier, Nadine Joachimowicz, et al.. Non-destructive Control of Fruit Quality via Millimeter Waves and Classification Techniques. IEEE Antennas and Propagation Magazine, Institute of Electrical and Electronics Engineers, 2020, 62 (5), pp.43-54. ⟨10.1109/MAP.2020.3003222⟩. ⟨hal-02926168⟩



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