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

Yoon, Hyo In; Lee, Hyein; Yang, Jung‐Seok; Choi, Jae-Hyeong; Park, Soo Hyun; Park, Yun Ji; Park, Jai-Eok; Kim, Sang Min; Park, Soo Hyun

doi:10.3390/agriculture13081477

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…Light gradient boosting machine (LightGBM), a decision tree ensemble model built on a gradient boosting framework, employs the histogram-based tree learning algorithm to significantly enhance training efficiency [42]. This algorithm introduces the leaf-wise growth strategy, also known as the best-first strategy, with the aim of optimizing the balance between model complexity and predictive accuracy [43].…”

Section: Modeling Methodsmentioning

confidence: 99%

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics

Huang,

Yuan,

Yang

et al. 2024

Foods

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Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower’s planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384–1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky–Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.

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Section: Modeling Methodsmentioning

confidence: 99%

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics

Huang,

Yuan,

Yang

et al. 2024

Foods

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“…The most commonly used are integrated models of decision trees, such as the bagging model: random forest (RF), and boosting model: gradient-boosting machine (GBM), etc. Compared with the boosting model, the bagging model cannot improve model deviation or significantly im-prove performance; the processing of unbalanced datasets is limited [21][22][23][24]. This paper chooses the gradient-boosting machine model in the boosting model, there are mainly gradient-boosting decision trees (GBDTs), extreme gradient-boosting (XGBoost), and light gradient-boosting machines (LightGBMs).…”

Section: Intelligent Evaluation Methods Of Asphalt Pavement Anti-skid...mentioning

confidence: 99%

Reconstruction and Intelligent Evaluation of Three-Dimensional Texture of Stone Matrix Asphalt-13 Pavement for Skid Resistance

Dai,

Luo,

Chen

et al. 2023

Lubricants

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To examine the three-dimensional texture structure of SMA-13 asphalt pavement and assess its anti-skid performance, a light gradient-boosting machine evaluation model was developed using non-contact three-dimensional laser-scanning technology. The study focused on collecting three-dimensional texture data from newly laid SMA-13 asphalt pavement. Subsequently, wavelet transform was employed to reconstruct the pavement’s three-dimensional texture, and discrete Fourier transform was utilized to separate macro- and microtextures, enabling the calculation of their characteristics. The macro- and micro-characteristics of the three-dimensional texture and friction coefficient were input into the model. A comparative analysis with linear regression and a random forest model revealed superior accuracy and efficiency in the model. The training set R2 is 0.948, and the testing set R2 is 0.842, effectively enabling the evaluation of pavement anti-skid performance. An analysis of parameter importance indicated that Rku and MPD are still effective indicators for evaluating skid resistance. Furthermore, diverse texture indexes exhibited varying effects on the anti-skid performance. The established asphalt pavement anti-skid evaluation model serves as a theoretical foundation for understanding the actual influence on pavement anti-skid performance.

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“…Integrated learning entails combining multiple weakly supervised models to obtain a more comprehensive and strongly supervised model. In this study, the PLSR algorithm was utilized to determine the optimal preprocessing method for the spectra, and two integrated learning models, SVM regressionadaptive enhancement (AdaBoost) and XGBoost, were XGBoost is an integrated learning algorithm based on gradient boosting machines (Yoon et al, 2023). It trains multiple weak classifiers iteratively, progressively improving model performance by minimizing the loss function.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques

Liang,

Tian,

et al. 2024

Journal of Food Science

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Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible–near‐infrared (Vis–NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis–NIR, NIR, and the fused Vis–NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)‐AdaBoost and XGBoost models were built based on the low‐level fusion (LLF) and intermediate‐level fusion (ILF) of single Vis–NIR and NIR as well as the fused data. The results showed that the SVM‐AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

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

Cited by 10 publications

References 31 publications

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics

Reconstruction and Intelligent Evaluation of Three-Dimensional Texture of Stone Matrix Asphalt-13 Pavement for Skid Resistance

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques

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