Wheat Quality Monitoring Using Ecological Factors And Remote Sensing Regionalization

Ecological factors play an important role in affecting the grain quality of wheat. Ecological factors and remote sensing division are used to monitor the grain protein content of wheat through remote sensing data, in order to improve monitoring accuracy. Winter wheat in Beijing was taken as a case study; the main studies were as follows:(1)The preliminary screening analysis for key ecological factors of the grain protein content of winter wheat based on neural networks was carried out. For affecting the percentage of the protein content of winter wheat, the neural network algorithm was adopted to determine the influence of various factors on the grain quality of winter wheat. (2)Various factors and their corresponding weights were utilized to establish one division model, while these factors were only used to build another model. Based on the two division models, key ecological factors of the grain quality of winter wheat were used to select the optimization model to achieve the classification division of the grain quality of the high-quality winter wheat in Beijing. (3) The model of monitoring the grain quality of winter wheat was established by using ecological factors and remote sensing information for different divisions. It was compared with the remote sensing monitoring model and ecological factor model. The optimization model of monitoring the grain quality of winter wheat was adopted to monitor the grain quality of winter wheat in Beijing using remote sensing data. The study has a great significance in enhancing the remote sensing monitoring mechanism of the grain quality of winter wheat and improving monitoring accuracy.The main results were as follows:1. The preliminary analysis for key ecological factors of the grain protein content of winter wheat based on neural networks.The meteorological data and soil nutrient data of the most representative cultivation sites of winter wheat in Beijing were used to evaluate the relative importance of the grain protein content of winter wheat affected by temperature, precipitation, light and soil nutrient content, respectively based on the neural network method. The study showed that the main factors affecting the protein content of winter wheat in Beijing were the light duration in the dough stage (June 6th to June 10th), days of temperature above 32℃, the soil nitrogen content, the average temperature in the whole filling stage (early May to early June), the average temperature in the late filling stage (May 26th to May30th), the accumulated temperature above 0℃in the late filling stage(late May to early June), the average temperature in the milk stage(June 1 st to June 5th), the temperature in the late filling stage(late May to early June), the precipitation and soil organic matter content(late May to early June). Key ecological factors were adopted as the inputs of the neural network model. The model was used to produce a response curve to reflect the trend of the protein content.2. Study on the grain quality division of winter wheat in Beijing based on remote sensing and geographic information system.Under the support of ArcGIS, various key ecological factors of the protein content of winter wheat were used to make the spatial interpolation, and then the point-like key factors were interpolated to the raster and the multi-factors spatial database were established. Each factor was set a weight for its RATIO calculated by the neural network method. The meteorological factors and soil factors were analyzed in the ENVI environment, and then the division results derived from the equal weight and different weight models were compared. Different classifications of various divisions of the grain quality of winter wheat in Beijing were obtained; these results were analyzed.3. Remote sensing monitoring for the protein content in key periods of the grain quality of winter wheat based on divisions.The grain protein of winter wheat, Zhongyou 206 in Beijing was chosen as the research objective. Various vegetation indices (VIs) and the grain protein of zhongyou 206 in a variety of different growth stages of winter wheat in Beijing were calculated by using remote sensing data to study their correlation. The results showed that NDVIgreen in May 11th was best correlated with the grain protein, and the highly significance was achieved, thus, this period is the best phase for monitoring the grain quality of winter wheat using remote sensing. The ecological environmental data, spectral data were used to construct the spectral quality model, the ecological environmental quality model and the comprehensive model of spectral and ecological environmental quality. F test was used for these three models, and these models reached the highly significant level; Compared with the spectral quality model and the ecological environmental quality model, the coefficient of determination (R2) of the comprehensive model of spectral and ecological environmental quality had significantly improved, and the relative root mean square error (RRMSE) and relative error (RE) had sharply deceased. It was demonstrated that the comprehensive model of spectral and ecological environmental quality was obviously better than another two models. In the study of constructing model of the five divisions, the accuracy of sub-models had different degrees of increase compared with the whole model. The prediction accuracies in the most high-quality division, the second-class division, the third-class division, the fourth-class division and the worst quality division were 91.6%,89.3%,85.6% 83.6% and 92.2% respectively. In the comparison to the whole model, The predicting accuracies of the models constructed by using divisions had increased by 12%,10.5%,7.3%,11.7% and 14.3% respectively. Therefore, remote sensing and ecological environmental data were used to construct the model to estimate the grain quality of winter wheat under the different divisions. It was feasible and highly accurate.The innovations of this study include:1. the nonlinear neural network method is adopted to study the complex relationships between the protein content of winter wheat and ecological factors.2. The division model of the grain quality of winter wheat is constructed through key ecological factors and their weights. The quality division is more scientific and reasonable.3. The remote sensing monitoring method for the grain quality of winter wheat, combined with ecological factors and remote sensing data, is proposed, and an comprehensive model is constructed to improve the monitoring accuracy of the grain protein content of winter wheat.