Determination of optimal harvest timing for field-grown apple fruits using hyperspectral imaging technology

EungChan Kim; Sang-Yeon Kim; Chang-Hyup Lee; Sungjay Kim; Xianghui Xin; Seul-Ki Lee; Jung-Gun Cho; Ghiseok Kim

doi:10.1117/12.3014570

6 June 2024 Determination of optimal harvest timing for field-grown apple fruits using hyperspectral imaging technology

EungChan Kim, Sang-Yeon Kim, Chang-Hyup Lee, Sungjay Kim, Xianghui Xin, Seul-Ki Lee, Jung-Gun Cho, Ghiseok Kim

Proceedings Volume 13060, Sensing for Agriculture and Food Quality and Safety XVI; 130600G (2024) https://doi.org/10.1117/12.3014570
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

We utilized hyperspectral imaging technology, which is commonly used for nondestructive quality assessment in agriculture, to predict SSC (Brix, %) and also the firmness (N) of apples. In this research, various regression models were applied based on machine learning and deep learning with hyperspectral (400~1000 nm) spectrum data to predict SSC and firmness of apple fruits. To evaluate the prediction accuracy of each model, coefficient of determination (r square) and Root Mean Square Error (RMSE) was used. For this purpose, spectral data of apple fruits was acquired and prediction models using various regression models such as PLSR were developed. Also, various preprocessing methods were applied, including extracting meaningful pixels, MSC (Multiplicative Scatter Correction), SNV (Standard Normal Variate), to enhance the accuracy of regression models. Through these process, SSC and firmness prediction performance of each model was analyzed and compared with various combination of preprocessing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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EungChan Kim, Sang-Yeon Kim, Chang-Hyup Lee, Sungjay Kim, Xianghui Xin, Seul-Ki Lee, Jung-Gun Cho, and Ghiseok Kim "Determination of optimal harvest timing for field-grown apple fruits using hyperspectral imaging technology", Proc. SPIE 13060, Sensing for Agriculture and Food Quality and Safety XVI, 130600G (6 June 2024); https://doi.org/10.1117/12.3014570

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