[Study on soil carbon estimation by on-the-go near-infrared spectra and partial least squares regression with variable selection].

The present paper tried to evaluate the effectiveness and improvement of variable selection before modeling with partial least squares regression (PLSR). Based on the independent test dataset, and compared with the PLSR model derived from all spectral variables, the prediction accuracy by modeling after variable selection has been improved. Thus, the results showed that variable selection was beneficial and necessary for soil carbon modeling by on-the-go NIRS. UVE (uninformative variable elimination) and UVE-SPA (successive projection algorithm) could perform effective variable selection and created promising models, and SPA and GA-PLS (genetic algorithm PLS) failed to make appropriate models. For synergy interval PLS (siPLS), change in interval number and number of interval for modeling could affect the prediction accuracy obviously. Promising models could be made by selecting appropriate interval number and number of interval for modeling, and siPLS could achieve similar prediction accuracy to UVE or UVE-SPA, and the shortcoming was that siPLS required a lot of computing time to find optimal combination of intervals for modeling.

[Diagnosis study of rice leaf under phosphorus insufficiency based on spectral features of scan image and pattern recognition].

Insufficiency of phosphorus could greatly effect rice production, thus it is significant to adopt quick and nondestructive diagnosis of phosphorus content. The present paper focused on first expanded leaves with different phosphorus fertilization levels, comprehensively extracted 26 features' spectral information such as color, texture and shape etc. Single feature index analysis was conducted. Then features were collected to integrate CfsSubsetEval + Scattersearch method for optimizing, evaluation and choosing. Based on the feature selection for different leave positions, leaves in different phosphorus fertilization levels were finally classified into three grades (extremly insufficient, significant insufficient and normal) according to rough set theory. Results showed that the accuracy of recognition was very high while few phosphorus contained in the leaves. Moreover, the third expanded leaf is the best part for phosphorus-nutrient diagnosis.

[Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice].

The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.

[Study on relationship between on-the-go near-infrared spectroscopy and soil texture].

Near infrared spectroscopy (NIRS) is a rapid, pioximal-sensed method that has proven useful in quantifying soil constituents mainly in laboratory. However, very little is known about how NIRS performs in a field setting by newly developed on-the-go NIRS measurements. The objective of the present study was to evaluate the relationship between on-the-go field NIRS measurements and soil texture in a glacial till soil. It was found that NIRS band combination based on difference, normalized difference and ratio could apparently improve the coefficient of relationship between NIRS and soil texture, and this might be a new and effective analytical procedure for field NIRS measurements.