Name
Abstract | ||
|---|---|---|
Measurement of soil total nitrogen (STN) is urgently important concerning requirement of precise and quantitative fertilizer application. To overcome the shortage of routine chemical detection, vis- short wavelength near infrared spectroscopy (Vis-SWNIRS) was employed as an accurate, cheap and timely alternative. Kennard-Stone algorithm was utilized for sample set partitioning, where 22 of the 32 samples were selected as calibration set, and the remaining 10 were included in validation set. No outliers were discovered under criterion based on spectral Mahalanobis distance and Dixon testing. Partial least squares regression (PLSR) was used to build STN detection model based on Vis-SWNIRS with cross validation by leave-one-out method. As a result, 5 principal components turned to be optimal considering model performance, where correlation coefficients for calibration and validation were 0.9724 and 0.8691, respectively, with PRESS (Prediction Residual Error Sum of Square) of 0.0684. It is feasible to detect STN by Vis-SWNIR spectroscopy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-18369-0_20 | COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE IV, PT 4 |
Keywords | Field | DocType |
Soil, Total Nitrogen, Vis- Short Wavelength Near Infrared Spectroscopy, Data Set Assignment, Outlier Detection | Anomaly detection,Pattern recognition,Partial least squares regression,Outlier,Mahalanobis distance,Artificial intelligence,Spectroscopy,Statistics,Cross-validation,Mathematics,Calibration,Principal component analysis | Conference |
Volume | Issue | ISSN |
347 | PART 4 | 1868-4238 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoze Feng | 1 | 2 | 1.13 |
| Xiaoyu Li | 2 | 0 | 1.35 |
| Wei Wang | 3 | 2 | 0.79 |
| Changju Liu | 4 | 0 | 0.34 |