Hyperspectral imaging and machine learning for monitoring produce ripeness

Riley D. Logan; Bryan Scherrer; Jacob Senecal; Neil S. Walton; Amy Peerlinck; John W. Sheppard; Joseph A. Shaw

doi:10.1117/12.2560968

22 April 2020 Hyperspectral imaging and machine learning for monitoring produce ripeness

Riley D. Logan, Bryan Scherrer, Jacob Senecal, Neil S. Walton, Amy Peerlinck, John W. Sheppard, Joseph A. Shaw

Proceedings Volume 11421, Sensing for Agriculture and Food Quality and Safety XII; 114210O (2020) https://doi.org/10.1117/12.2560968
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process that uses a visible near-infrared (VNIR) hyperspectral imager from Resonon, Inc., coupled with machine learning algorithms to assess the ripeness of various pieces of produce. The images were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using RGB images, full spectrum hyperspectral images, and the genetic algorithm feature selection method. Results showed that the genetic algorithm-based feature selection method outperforms RGB images for all tested produce, outperforms hyperspectral imagery for bananas, and matches hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multi-spectral imager for use in monitoring produce in grocery stores.

Conference Presentation

Citation Download Citation

Riley D. Logan, Bryan Scherrer, Jacob Senecal, Neil S. Walton, Amy Peerlinck, John W. Sheppard, and Joseph A. Shaw "Hyperspectral imaging and machine learning for monitoring produce ripeness", Proc. SPIE 11421, Sensing for Agriculture and Food Quality and Safety XII, 114210O (22 April 2020); https://doi.org/10.1117/12.2560968

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