ENGINEERING

Potential of multispectral imaging for maturity classification and recognition of oriental melon

Seongmin Lee, Kyoung-Chul Kim*, Kangjin Lee, Jinhwan Ryu, Youngki Hong, Byeong-Hyo Cho*

Department of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Korea

*Corresponding author: kkcmole@korea.kr, cho2519@korea.kr

Abstract

In this study, we aimed to apply multispectral imaging (713 – 920 nm, 10 bands) for maturity classification and recognition of oriental melons grown in hydroponic greenhouses. A total of 20 oriental melons were selected, and time series multispectral imaging of oriental melons was 7 – 9 times for each sample from April 21, 2023, to May 12, 2023. We used several approaches, such as Savitzky-Golay (SG), standard normal variate (SNV), and Combination of SG and SNV (SG + SNV), for pre-processing the multispectral data. As a result, 713 – 759 nm bands were preprocessed with SG for the maturity classification of oriental melons. Additionally, a Light Gradient Boosting Machine (LightGBM) was used to train the recognition model for oriental melon. R2 of recognition model were 0.92, 0.91 for the training and validation sets, respectively, and the F-scores were 96.6 and 79.4% for the training and testing sets, respectively. Therefore, multispectral imaging in the range of 713 – 920 nm can be used to classify oriental melons maturity and recognize their fruits.

Keywords

correlation analysis, LightGBM (Light Gradient Boosting Machine), nearinfrared, Savitzky-Golay

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