Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning DOI Creative Commons
Yafeng Li, Xingang Xu,

Wenbiao Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4479 - 4479

Published: Nov. 29, 2024

Nitrogen is the main nutrient element in growth process of white radish, and accurate monitoring radish leaf nitrogen content (LNC) an important guide for precise fertilization decisions field. Using LNC as object, research on hyperspectral estimation methods was carried out based field sample data at multiple stages using feature selection integrated learning algorithm models. First, Vegetation Index (VI) constructed from data. We extracted sensitive features VI response to Pearson’s feature-selection approach. Second, a stacking-integrated approach proposed machine algorithms such Support Vector Machine (SVM), Random Forest (RF), Ridge K-Nearest Neighbor (KNN) base model first layer architecture, Lasso meta-model second realize LNC. The analysis results show following: (1) bands are mainly centered around 600–700 nm 1950 nm, VIs also concentrated this band range. (2) Stacking with spectral inputs achieved good prediction accuracy leaf, R2 = 0.7, MAE 0.16, MSE 0.05 estimated over whole stage radish. (3) variable filtering function chosen meta-model, which has redundant model-selection effect helps improve quality framework. This study demonstrates potential method stages.

Language: Английский

Rapid and nondestructive identification of rice storage year using hyperspectral technology DOI
Xiaorong Sun,

Xinpeng Zhou,

Cuiling Liu

et al.

Food Control, Journal Year: 2024, Volume and Issue: 168, P. 110850 - 110850

Published: Sept. 3, 2024

Language: Английский

Citations

5
Stacking Ensemble Learning Method for Quantitative Analysis of Soluble Solid Content in Apples DOI
Lixin Zhang, Zhensheng Huang, Xiao Zhang

et al.

Journal of Chemometrics, Journal Year: 2025, Volume and Issue: 39(1)

Published: Jan. 1, 2025

ABSTRACT The soluble solids content (SSC) in apples directly affects their quality. This study aimed to detect SSC nondestructively using hyperspectral technology combined with chemometrics. However, data generation may not follow a specific pattern, and even small perturbations the can have significant impact on constructed model. To improve anti‐interference capability of individual models, this proposed stacking ensemble learning method that adopted partial least squares (PLS), support vector machine (SVM), extreme gradient boosting (Xgboost), random forest (RF) as basic‐learners, RF serving meta‐learner. Experimental results showed performance established model test set were follows: root mean square error (RMSE) was 0.4325, absolute (MAE) 0.3245, percentage (MAPE) 0.0271, coefficient determination () 0.9250. These indicate approach could appropriately fuse predictive each basic‐learner prediction accuracy models. verify superiority method, selection its meta‐learner, combination strategy compared analyzed. only provides theoretical reference for further development related nondestructive detection equipment but also offers guidance fusion algorithms well.

Language: Английский

Citations

0
On-site quality control of Hypericum perforatum L. in the Xinjiang Region of China using a portable near-infrared spectrometer DOI
Zhiyong Zhang,

Jiahe Qian,

Shamukaer Alimujiang

et al.

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 112848 - 112848

Published: Jan. 1, 2025

Language: Английский

Citations

0
Research on Rapid and Non-Destructive Detection of Adulterated Wheat Flour Using Hyperspectral Imaging and Machine Learning DOI
G H Bai,

Tingsong Zhang,

Ziyuan Liu

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Application of artificial intelligence in the rapid determination of moisture content in medicine food homology substances DOI

Mengyu Zhang,

Boran Lin,

Shudi Zhang

et al.

Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 143905 - 143905

Published: March 1, 2025

Language: Английский

Citations

0
Explaining relationships between chemical structure and tar-rich coal pyrolysis products yield based on Pearson correlation coefficient DOI
Zhi Zhang, Anning Zhou, Zhiwei Shi

et al.

Fuel, Journal Year: 2025, Volume and Issue: 395, P. 135029 - 135029

Published: March 27, 2025

Language: Английский

Citations

0
Combining Feature Extraction Methods and Categorical Boosting to Discriminate the Lettuce Storage Time Using Near-Infrared Spectroscopy DOI Creative Commons
Xuan Zhou, Xiaohong Wu, Zheng Cao

et al.

Foods, Journal Year: 2025, Volume and Issue: 14(9), P. 1601 - 1601

Published: May 1, 2025

Lettuce is a kind of nutritious leafy vegetable. The lettuce storage time has significant impact on its nutrition and taste. Therefore, to classify samples with different times accurately non-destructively, this study built classification models by combining several feature extraction methods categorical boosting (CatBoost). Firstly, the near-infrared (NIR) spectral data were collected using NIR spectrometer, then they preprocessed six preprocessing methods. Next, was carried out approximate linear discriminant analysis (ALDA), common-vector (CLDA), maximum-uncertainty (MLDA), null-space (NLDA). These four can solve problem small sample sizes. Finally, achieved regression trees (CARTs) CatBoost, respectively. experimental results showed that accuracy NLDA combined CatBoost could reach 97.67%. combination (NLDA) spectroscopy an effective way time.

Language: Английский

Citations

0
Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning DOI Creative Commons
Yafeng Li, Xingang Xu,

Wenbiao Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4479 - 4479

Published: Nov. 29, 2024

Nitrogen is the main nutrient element in growth process of white radish, and accurate monitoring radish leaf nitrogen content (LNC) an important guide for precise fertilization decisions field. Using LNC as object, research on hyperspectral estimation methods was carried out based field sample data at multiple stages using feature selection integrated learning algorithm models. First, Vegetation Index (VI) constructed from data. We extracted sensitive features VI response to Pearson’s feature-selection approach. Second, a stacking-integrated approach proposed machine algorithms such Support Vector Machine (SVM), Random Forest (RF), Ridge K-Nearest Neighbor (KNN) base model first layer architecture, Lasso meta-model second realize LNC. The analysis results show following: (1) bands are mainly centered around 600–700 nm 1950 nm, VIs also concentrated this band range. (2) Stacking with spectral inputs achieved good prediction accuracy leaf, R2 = 0.7, MAE 0.16, MSE 0.05 estimated over whole stage radish. (3) variable filtering function chosen meta-model, which has redundant model-selection effect helps improve quality framework. This study demonstrates potential method stages.

Language: Английский

Citations

1