Predicting Models for Plant Metabolites Based on PLSR, AdaBoost, XGBoost, and LightGBM Algorithms Using Hyperspectral Imaging of Brassica juncea

Hyo In Yoon, Hyein Lee, Jung-Seok Yang, Jae-Hyeong Choi, Dae-Hyun Jung, Yun Ji Park, Jai-Eok Park, Sang Min Kim and Soo Hyun Park ()
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Hyo In Yoon: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Hyein Lee: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Jung-Seok Yang: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Jae-Hyeong Choi: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Dae-Hyun Jung: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Yun Ji Park: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Jai-Eok Park: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Sang Min Kim: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea
Soo Hyun Park: Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Saimdang-ro 679, Gangneung 25451, Republic of Korea

Agriculture, 2023, vol. 13, issue 8, 1-12

Abstract: The integration of hyperspectral imaging with machine learning algorithms has presented a promising strategy for the non-invasive and rapid detection of plant metabolites. For this study, we developed prediction models using partial least squares regression (PLSR) and boosting algo-rithms (such as AdaBoost, XGBoost, and LightGBM) for five metabolites in Brassica juncea leaves: total chlorophyll, phenolics, flavonoids, glucosinolates, and anthocyanins. To enhance the model performance, we employed several spectral data preprocessing methods and feature-selection al-gorithms. Our results showed that the boosting algorithms generally outperformed the PLSR models in terms of prediction accuracy. In particular, the LightGBM model for chlorophyll and the AdaBoost model for flavonoids improved the prediction performance, with R2p = 0.71–0.74, com-pared to the PLSR models (R2p = 0.53–0.58). The final models for the glucosinolates and anthocya-nins performed sufficiently for practical uses such as screening, with R2p = 0.82–0.85 and RPD = 2.4–2.6. Our findings indicate that the application of a single preprocessing method is more effective than utilizing multiple techniques. Additionally, the boosting algorithms with feature selection ex-hibited superior performance compared to the PLSR models in the majority of cases. These results highlight the potential of hyperspectral imaging and machine learning algorithms for the non-destructive and rapid detection of plant metabolites, which could have significant implications for the field of smart agriculture.

Keywords: prediction models; hyperspectral image; PLSR model; AdaBoost; XGBoost; LightGBM (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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