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

Flora Zidane (1) , Jérôme Lanteri (1) , Laurent Brochier (1) , N. Joachimowicz (2) , Hélène Roussel (2) , Claire Migliaccio (1)

1 LEAT - Laboratoire d'Electronique, Antennes et Télécommunications
2 GeePs - Laboratoire Génie électrique et électronique de Paris

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.

Mots clés: Millimeter-Wave imaging; Millimeter-Wave measurements; 2D-Fourier-transform; Support Vector Machine

Domaines: Electromagnétisme

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, 2020, pp.1-1. ⟨10.1109/TAP.2020.3016184⟩. ⟨hal-02925919⟩