Assessing produce freshness using hyperspectral imaging and machine learning

Abstract

A method of monitoring produce freshness with hyperspectral imaging and machine learning is described as a way to reduce food waste in grocery stores. The method relies on hyperspectral reflectance images in the visible–near-infrared spectral range from 387.12 to 1023.5 nm with a 2.12-nm spectral resolution. The images were recorded in a laboratory with the imager viewing produce samples illuminated by broadband halogen lights, but we also recorded and discussed the implications of the illumination spectrum of lights found in a variety of grocery stores. A convolutional neural network was used to perform freshness classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm (GA) was used to determine the wavelengths carrying the most useful information for age classification, with an eye toward a future multispectral imager. Hyperspectral images were processed to explore the use of RGB images, GA-selected multispectral images, and full-spectrum hyperspectral images. The GA-based feature selection method outperformed RGB images for all tested produce, outperformed hyperspectral imagery for bananas, and matched hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores.