Research On Non-destructive And Rapid Acquisition Technique For Rice Physiological Characteristics And Growth Information

Precision agriculture is the inevitable trend for the development of agricultural in the 21st century, and it is an important way for achieving low energy consumption, high efficiency, high quality and security. The key technology includes field information acquisition, information management and decision-making, and the feature of variable operating, which means precision agriculture can be processed relying on the existence of in-field variability. So far, how to quickly catch real-time status information of soil and crops growth information is one of the most critical issues.Based on the research on the application of spectroscopy in biosystem engineering field, this dissertion focuses on the spectral investigation on rice concerning the quality, nutrient and disease, etc. And validate the feasibility of designing non-destructive equipment for the field application for plant monitoring and diagnosis.This thesis designed an experimental plan regarding the methodology of quadratic orthogonal regression and the set-up of differential level of fertilizers, especially the nitrogen. The visible-near infrared spectroscopy was adopted to build the relationship between the reflectance characteristics of rice canopy or leaf and the SPAD values or nitrogen content of rice canopy or leaf, and the relationship between the reflectance characteristics of rice leaf and the chlorophyll content or trace element content of rice leaf. The spectral characteristic of infected rice leaf was also evaluated concerning the relation between the disease and shortage of nutrient. The thesis later conducted field experiments to prove the feasibility of using rice canopy spectral information to predict soil nutrients (nitrogen, phosphorus, kalium). The correlation between the SPAD value or nitrogen content with the multi-spectral images of rice plant and leaf was also studied. In addition, a preliminary study of the irradiation treatment on the rice reflectance characteristics was studied, and the internal components for irradiated grain (amylase and proteins) was predicted combined with the mid-infrared spectroscopy.The conclusions for this thesis are as follows:1) With the methodology of Chemometrics combined with the sensitive waveband acquisition, the thesis built the model for SPAD value prediction for canopy and leaf of rice. The prediction results showed that, with respect to SPAD values of rice canopy, the prediction accuracy for nonlinear PLS-LS-SVM model is higher, while for the prediction model for SPAD value of rice leaf, the model with the full wavelengths is better. Taking the advantage of preprocessing method and sensitive waveband acquisition together with the data compression, the thesis developed the model for predicting chlorophyll content. The optimal wavelength selection method combined DOSC with SPA is more accurate than MSC combined with SPA for chlorophyll content prediction.2) Based on the ICA, the relationship between the reflectance characteristics and the nitrogen content of rice canopy or leaf was investigated. The prediction model for nitrogen content of rice canopy was established, and the model with the full wavelengths has the highest prediction accuracy. The prediction model for the nitrogen content of rice leaf, the prediction accuracy based on ICA-LS-SVM model is higher and can be taked as preliminary reference for machine development. PLS models were established based on the full wavelengths, characteristic wavebands, and characteristic wavelengths to distinguish the infected rice leaves. The results showed that, the model with full wavelengths had the highest identification rate. The ICA-LS-SVM model built for identification of infected leaf can be as high as 86.7%.3) The thesis firstly took research on the trace elements investigation with technology of spectroscopy and data mining. The prediction model based on full wavelengths, characteristic wavebands and characteristic wavelengths were established by the PLS model. For the trace elements Fe, the prediction accuracy based on full wavelengths was higher than using characteristic wavebands, higher than model with characteristic wavelengths. For the trace elements Zn, the model based on characteristic wavelengths was higher than full wavelengths model, and higher than using characteristic wavebands. The prediction model for trace elements Fe and Zn based on the ICA-LS-SVM models were established. It indicated that, the prediction accuracy using independent component analysis (ICA) combined with nonlinear LS-SVM regression model higher than PLS-LS-SVM model, higher than linear PLS model.4) The feasibility of using rice canopy spectral information to evaluate soil nutrients (nitrogen, phosphorus, kalium) was investigated. The prediction accuracy for soil nutrients (nitrogen, phosphorus, kalium) based on PLS model in rice booting stage was higher than in tillering stage. The prediction accuracy for nitrogen content is higher than phosphorus content and the results for kalium is relatively poor. The prediction results for soil nitrogen, phosphorus and kalium in rice tillering and booting stage showed, models based on nonlinear PLS-LS-SVM is better than ICA-LS-SVM, PLS-BPNN and PLS models. The prediction model for soil nitrogen content based on vegetation index is better than ICA-LS-SVM model.5) The relationship between the vegetation index and the SPAD values or nitrogen content of rice canopy or leaf was studied based on the multi-spectral imaging technique. The vegetation index used is the normalized difference vegetation index, green normalized vegetation index and ratio vegetation index. For the SPAD value of rice leaf, the correlation coefficient is reached 0.8756 for the prediction model. It showed that for SPAD value of the rice plant, the prediction results in rice booting stage was better than the tillering stage. For the nitrogen content of rice plant, the prediction accuracy in rice tillering stage was higher than the booting stage.6) The age discrimination model for rice was built and evaluated, and the radiation dose prediction for grain was also investigated. The prediction model for internal content (amylase and protein) of irradiated grain was studied. The differential rate of 100% was reached for the age prediction of grain based on the independent component analysis (ICA) combine with BP neural network model. The results for different irradiation doses prediction of grain showed that LS-SVM model was better than PLS model. For the amylase content prediction model of irradiated grain, models based on near infrared spectroscopy was better than the mid-infrared spectroscopy, and the prediction model with LS-SVM was superior to PLS model. For the protein content prediction model of irradiated grain, models based on mid-infrared spectroscopy was better than the near infrared spectroscopy, and the prediction model with LS-SVM was also superior to PLS model.