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

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4479 - 4479

Опубликована: Ноя. 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.

Язык: Английский

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

Xinpeng Zhou,

Cuiling Liu

и другие.

Food Control, Год журнала: 2024, Номер 168, С. 110850 - 110850

Опубликована: Сен. 3, 2024

Язык: Английский

Процитировано

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

и другие.

Journal of Chemometrics, Год журнала: 2025, Номер 39(1)

Опубликована: Янв. 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.

Язык: Английский

Процитировано

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

и другие.

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 112848 - 112848

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

Mengyu Zhang,

Boran Lin,

Shudi Zhang

и другие.

Food Chemistry, Год журнала: 2025, Номер unknown, С. 143905 - 143905

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

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

и другие.

Fuel, Год журнала: 2025, Номер 395, С. 135029 - 135029

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

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

и другие.

Foods, Год журнала: 2025, Номер 14(9), С. 1601 - 1601

Опубликована: Май 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.

Язык: Английский

Процитировано

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

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4479 - 4479

Опубликована: Ноя. 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.

Язык: Английский

Процитировано

1