Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

Liu, Yang; Wang, Honghong; Fei, Yeqi; Liu, Ying; Shen, Luxiang; Zhuang, Zilong; Zhang, Xiao

doi:10.3390/s21030930

Cited by 18 publications

(9 citation statements)

References 19 publications

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“…The diagram and other detailed parameters (e.g. the spectral resolution was 2.8 nm and image resolution was 800 × 664 pixels) of this HSI system can be found in a previous work [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

Jiang

et al. 2022

Molecules

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The maturity of Camellia oleifera fruit is one of the most important indicators to optimize the harvest day, which, in turn, results in a high yield and good quality of the produced Camellia oil. A hyperspectral imaging (HSI) system in the range of visible and near-infrared (400–1000 nm) was employed to assess the maturity stages of Camellia oleifera fruit. Hyperspectral images of 1000 samples, which were collected at five different maturity stages, were acquired. The spectrum of each sample was extracted from the identified region of interest (ROI) in each hyperspectral image. Spectral principal component analysis (PCA) revealed that the first three PCs showed potential for discriminating samples at different maturity stages. Two classification models, including partial least-squares discriminant analysis (PLS-DA) and principal component analysis discriminant analysis (PCA-DA), based on the raw or pre-processed full spectra, were developed, and performances were compared. Using a PLS-DA model, based on second-order (2nd) derivative pre-processed spectra, achieved the highest results of correct classification rates (CCRs) of 99.2%, 98.4%, and 97.6% in the calibration, cross-validation, and prediction sets, respectively. Key wavelengths selected by PC loadings, two-dimensional correlation spectroscopy (2D-COS), and the uninformative variable elimination and successive projections algorithm (UVE+SPA) were applied as inputs of the PLS-DA model, while UVE-SPA-PLS-DA built the optimal model with the highest CCR of 81.2% in terms of the prediction set. In a confusion matrix of the optimal simplified model, satisfactory sensitivity, specificity, and precision were acquired. Misclassification was likely to occur between samples at maturity stages two, three, and four. Overall, an HSI with effective selected variables, coupled with PLS-DA, could provide an accurate method and a reference simple system by which to rapidly discriminate the maturity stages of Camellia oleifera fruit samples.

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“…The diagram and other detailed parameters (e.g. the spectral resolution was 2.8 nm and image resolution was 800 × 664 pixels) of this HSI system can be found in a previous work [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

Jiang

et al. 2022

Molecules

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“…The LR algorithm is often thought of as a traditional algorithm, but is essentially a form of machine learning ( 34 ). The XGBoost is a ML approach that has the unique ability to integrate missing data quickly and flexibly, as well as to assemble poor prediction models into a more accurate one ( 35 , 36 ). The RF is a ML classifier that employs multiple trees to train and predict samples.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer

Ding

Wang

Zhang

et al. 2022

Front. Public Health

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Objective:Distant metastasis other than non-regional lymph nodes and lung (i.e., M1b stage) significantly contributes to the poor survival prognosis of patients with germ cell testicular cancer (GCTC). The aim of this study was to develop a machine learning (ML) algorithm model to predict the risk of patients with GCTC developing the M1b stage, which can be used to assist in early intervention of patients.MethodsThe clinical and pathological data of patients with GCTC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Combing the patient's characteristic variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression(LR), eXtreme Gradient Boosting (XGBoost), light Gradient Boosting Machine (lightGBM), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor (kNN). Model performances were evaluated by 10-fold cross-receiver operating characteristic (ROC) curves, which calculated the area under the curve (AUC) of models for predictive accuracy. A total of 54 patients from our own center (October 2006 to June 2021) were collected as the external validation cohort.ResultsA total of 4,323 patients eligible for inclusion were screened for enrollment from the SEER database, of which 178 (4.12%) developing M1b stage. Multivariate logistic regression showed that lymph node dissection (LND), T stage, N stage, lung metastases, and distant lymph node metastases were the independent predictors of developing M1b stage risk. The models based on both the XGBoost and RF algorithms showed stable and efficient prediction performance in the training and external validation groups.ConclusionS-stage is not an independent factor for predicting the risk of developing the M1b stage of patients with GCTC. The ML models based on both XGBoost and RF algorithms have high predictive effectiveness and may be used to predict the risk of developing the M1b stage of patients with GCTC, which is of promising value in clinical decision-making. Models still need to be tested with a larger sample of real-world data.

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“…At the same time, XGBoost also supports column sampling to avoid overfitting and reduce the computational workload. After each iteration, XGBoost assigns the learning rate to leaf nodes, reducing the weight of each tree and providing better space for subsequent learning ( Liu et al., 2021b ).…”

Section: Methodsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

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Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

Cited by 18 publications

References 19 publications

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

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