Prediction of Firmness of Sweetpotatoes using VNIR Hyperspectral Imaging and Machine Learning

Md Toukir Ahmed, Yuzhen Lu, Arthur Villordon, Mohammed Kamruzzaman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sweetpotatoes are used in a variety of ways and are regarded as a commercially important agricultural product due to their exceptional nutrition. Consumer perception is greatly influenced by the sweetpotato's physicochemical characteristics, such as firmness. The traditional methods for measuring firmness are arduous, time-consuming, and destructive. Recently, hyperspectral imaging coupled with machine learning has been considered an innovative approach for quick and non-destructive analysis of agricultural and biological products. This study used both linear [partial least squares regression (PLSR), multiple linear regression (MLR)] and non-linear (support vector regression (SVR) machine learning-based regression methods to predict the firmness of sweetpotatoes of different varieties using spectral data extracted from the images collected using a portable visible near-infrared hyperspectral imaging system (400-1000 nm). Important feature wavelengths were identified using the recursive feature elimination (RFE) technique to show the pixel-wise distribution of firmness of the sweetpotatoes and to aid the development of a low-cost multispectral system for industrial application. The results of the predictive analysis suggested that visible near-infrared hyperspectral imaging technology and machine learning could provide a quick and non-invasive prediction of sweetpotato firmness.

Original languageEnglish (US)
Title of host publication2023 ASABE Annual International Meeting
PublisherAmerican Society of Agricultural and Biological Engineers
ISBN (Electronic)9781713885887
DOIs
StatePublished - 2023
Event2023 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2023 - Omaha, United States
Duration: Jul 9 2023Jul 12 2023

Publication series

Name2023 ASABE Annual International Meeting

Conference

Conference2023 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2023
Country/TerritoryUnited States
CityOmaha
Period7/9/237/12/23

Keywords

  • Firmness
  • Hyperspectral image
  • Machine learning
  • MLR
  • PLSR
  • RFE
  • SVR
  • Sweetpotato

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Bioengineering

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