The Study On Bio-physical And Bio-chemical Parameters Of Processing Tomato Estimate By Canopy Hyperspectra Variables

Agriculture is the foundation of our national economy. Precision farming is the ultimate goal of Chinese modern agriculture development. The birth of hyperspectral remote sensing technology promotes the modern process of precision agriculture. Hyperspectral remote sensing has high spectral resolution (generally less than 10nm), strong waveband continuity and a great of spectral information, so the hyperspectral remote sensing technology has became a necessary tool of precision agriculture. The implication of hyperspectral remote sensing technology can not only large, real-time, non-destructive testing crop growth, but also provide a rapid and accurate access to information and the growth of crop growth environment. It has wide application foreground in precision farming.The purpose of this study Xinjiang Processing Tomato Ligeer87-5 and Shihong 206 as a research object, artificial irrigation different settings and different amount of nitrogen application under field trials. ASD-field radiation spectra was used to measure the appearance of processing tomato growing canopy reflectance spectra in natural conditions in different periods. According to unique spectral absorption, reflection and red edge features of Processing Tomato Canopy, the spectral variables, which may affect of processing tomato biophysical and biochemical, were established. Processing tomato biophysical, biochemical parameters of hyperspectral remote sensing models was estimated. The growth quality of Processing Tomato during its life cycle was detected. The results were as follows:1. The establishment of the Processing Tomato estimates biochemical parameters (chlorophyll, nitrogen and soluble sugar and lycopene, and so on) remote sensing estimation model. Hyperspectral of variables normalized difference vegetation index (NDVI) and the density of chlorophyll had a strong correlation. Among the remote sensing models of estimating the chlorophyll content, the density estimate chlorophyll content was the best model. This model had an accuracy of more than 80 percent, thus could truly reflect the canopy information of Processing Tomato. Estimation model of leaf nitrogen content had an accuracy of 60-70%. The model of estimating soluble sugar content had an accuracy of about 80%. Estimation of lycopene in tomato processing model had an accuracy of 65-70%.2. The establishment of the Processing Tomato estimate biophysical parameters (LAI, fresh ground biomass, plant moisture content, etc.) remote sensing models. The remote sensing model of estimating LAI had an accuracy of nearly 70 percent. The model of estimating aboveground biomass had an accuracy of nearly 70%. The remote sensing estimation model of estimating plant moisture test had an accuracy of 75%.