Next Article in Journal
Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
Previous Article in Journal
An Intelligent Tutoring System to Facilitate the Learning of Programming through the Usage of Dynamic Graphic Visualizations
Article

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

by 1,2, 1,2,*, 1,2, 1,2 and 1
1
School of Information and Computer Science, Anhui Agricultural University, Anhui 230036, China
2
Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Anhui 230036, China
*
Author to whom correspondence should be addressed.
Received: 13 January 2020 / Revised: 18 February 2020 / Accepted: 19 February 2020 / Published: 23 February 2020
(This article belongs to the Special Issue Advanced Spectroscopy-Based Technologies in Soil Monitoring)
The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky–Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration. View Full-Text
Keywords: visible near-infrared ray spectroscopy; soil-available potassium; pretreatment; regression model visible near-infrared ray spectroscopy; soil-available potassium; pretreatment; regression model
Show Figures

Figure 1

MDPI and ACS Style

Jin, X.; Li, S.; Zhang, W.; Zhu, J.; Sun, J. Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms. Appl. Sci. 2020, 10, 1520. https://0-doi-org.brum.beds.ac.uk/10.3390/app10041520

AMA Style

Jin X, Li S, Zhang W, Zhu J, Sun J. Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms. Applied Sciences. 2020; 10(4):1520. https://0-doi-org.brum.beds.ac.uk/10.3390/app10041520

Chicago/Turabian Style

Jin, Xiu, Shaowen Li, Wu Zhang, Juanjuan Zhu, and Jia Sun. 2020. "Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms" Applied Sciences 10, no. 4: 1520. https://0-doi-org.brum.beds.ac.uk/10.3390/app10041520

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop