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Damaged Apple Sorting with mmWave Imaging and Non-Linear Support Vector Machine

Abstract : This paper is a proof of concept proposing and describing a complete workflow to differentiate healthy from damaged apples, starting with mmWave measurements and ending with a classification based on Support Vector Machine. The method has proven to be successful with only 6% error when scan angle and frequency diversity are used. In a first step, we build a database of more than 1800 images obtained by processing measurements with a two-dimensional fast Fourier transform. Images are then converted to binary and used as the input to a non-linear SVM. At this stage, 90% of the database is used for training, and coefficients C and γ are tuned to minimize the error. The remaining 10% of images are used for testing. In a second step, we assess and discuss the influence of the physical inputs of the database: the frequency, the sparsity of measurement points and the size of the apples. Finally we explore new scenarios considering other fruits.
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Contributor : Sophie Gaffé-Clément Connect in order to contact the contributor
Submitted on : Monday, August 31, 2020 - 10:22:21 AM
Last modification on : Wednesday, March 23, 2022 - 3:40:47 AM



Flora Zidane, Jérôme Lanteri, Laurent Brochier, N. Joachimowicz, Hélène Roussel, et al.. Damaged Apple Sorting with mmWave Imaging and Non-Linear Support Vector Machine. IEEE Transactions on Antennas and Propagation, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. ⟨10.1109/TAP.2020.3016184⟩. ⟨hal-02925919⟩



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