Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning.

Abstract

China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440-530 nm and 680-780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple's equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.

Plant phenotyping relevance

リンゴ果実の糖濃度という植物器官形質を、蛍光ハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、モデル比較・特徴抽出・精度評価も行っている。

Code and data availability

The paper reports apple fluorescence hyperspectral imaging and machine learning for sucrose concentration prediction, but no public dataset, images, code, or trained model is deposited. The Data Availability Statement only offers inquiries to the corresponding author, so any paper-specific assets would require author接触

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