DoFP-ML: A Machine Learning Approach to Food Quality Monitoring Using a DoFP Polarization Image Sensor

Abstract

Good nutrition is an important part of leading a healthy lifestyle. This has brought into stark focus the need for efficient and low-cost methods for large scale food quality assessment. This article proposes a non-invasive and non-destructive system for estimating the freshness of apples using polarization images from a Division-of-Focal-Plane (DoFP) polarization camera. The proposed system uses Machine Learning Systems namely, Support Vector Regression (SVR) and Gaussian Process Regression (GPR), to estimate the age of apples and determine if they are fit for consumption even before the external rot appears on the fruit. Initially, the reconstructed images namely, Degree of Linear Polarization (DoLP) and Angle of Polarization (AoP), are generated from the polarization image and their respective correlations with the actual age of apples (in days) are established. These reconstructed images are then fed as input features to the Machine Learning Systems to ultimately estimate the age of the apples. Experiments on real data obtained from the DoFP camera show that the proposed system is non-destructive and capable of non-invasively estimating the age of the apple with an average accuracy of up to 92.57%.