Damaged Apple Sorting With mmWave Imaging and Nonlinear Support Vector Machine

Abstract

This article is a proof of concept proposing and describing a complete workflow to differentiate healthy from damaged apples, starting with millimeter-wave (mmWave) measurements and ending with a classification based on support vector machine (SVM). 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 2-D fast Fourier transform. Images are then converted to binary and used as the input to a nonlinear SVM. At this stage, 90% of the database is used for training, and coefficients ${C}$ and $\gamma $ 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.