Estimation of secondary soil properties by fusion of laboratory and on-line measured vis-NIR spectra

Visible and near infrared (vis-NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis-NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305-1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis-NIR spectroscopy

Near-Infrared Spectroscopy Technology for Soil Nutrients Detection Based on LS-SVM

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceThe detection method of the soil nutrients (organic matter and available N, P, K) were analyzed based on the near infrared spectroscopy technology in order to decision-making for precision fertilization. 54 samples with 7m×7m was collected using DGPS receiver positioning in a soybean field. The soil organic matter, available nitrogen (N), available phosphorus (P), available potassium (K) content was determined, the near-infrared diffuse reflectance spectrum of the soil samples were obtained by FieldSpec3 spectrometer. 54 samples were randomly divided into 40 prediction sets and 14 validation sets. After smoothing, the eight principal components of original spectra were extracted by principal component analysis (PCA). Prediction model of soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) were respectively established with the eight principal component as input and soil nutrients by measured as the output, and the 14 validation samples were predicted. The results showed that the soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) prediction model were set up with principal component analysis and LS-SVM, which the correlation coefficients between the prediction value and measurement value were 0.8708, 0.7206, 0.8421 and 0.6858, the relative errors of the LS-SVM prediction was smaller and those mean values were 1.09%, 1.06%, 4.08% and 0.69%. The method of soil organic matter content prediction is feasible

Near-infrared spectroscopy technology for Soil nutrients detection based on LS-SVM

Abstract: The detection method of the soil nutrients (organic matter and available N, P, K) were analyzed based on the near infrared spectroscopy technology in order to decision-making for precision fertilization. 54 samples with 7m×7m was collected using DGPS receiver positioning in a soybean field. The soil organic matter, available nitrogen (N), available phosphorus (P), available potassium (K) content was determined, the near-infrared diffuse reflectance spectrum of the soil samples were obtained by FieldSpec3 spectrometer. 54 samples were randomly divided into 40 prediction sets and 14 validation sets. After smoothing, the eight principal components of original spectra were extracted by principal component analysis (PCA). Prediction model of soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) were respectively established with the eight principal component as input and soil nutrients by measured as the output, and the 14 validation samples were predicted. The results showed that the soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) prediction model were set up with principal component analysis and LS-SVM, which the correlation coefficients between the prediction value and measurement value were 0.8708, 0.7206, 0.8421 and 0.6858, the relative errors of the LS-SVM prediction was smaller and those mean values were 1.09%, 1.06%, 4.08% and 0.69%. The method of soil organic matter content prediction is feasible