Spectral Features Of Apple Florescence And Fruit Stages And Estimation Of Fruit/Tree Ratio Based On Hyperspectral Data

Shandong Province is the main area for apple production, and Qixia City has the fine reputation of township of the apple. In recent years, it has higher demand to further enhance apple quality and improve apple production in international and domestic, therefore, the apple's advanced cultivation technology is more important in the world agricultural production. With the development of agricultural remote sensing and information technology application, remote sensing technology, especially hyperspectral remote sensing technology has been used more in the nutrition diagnosis, growth monitoring and yield estimation in wheat, corn, rice, cotton and other field crops, and achieved remarkable results, but it has less application in apple side. The spectral characteristics of apple has a good response to apple's growth, nutrition, production, quality and other status, and the canopy reflectance spectroscopy detection of the apple has great significance to production management. This study aims on the spectral features and sensitive spectrum wave band in the apple florescence and fruit.Taking Qixia City as the research region, using supervised classification of the flowering and fruit apple digital photos to extract the target information of the ratio of apple flower/leaf, apple flower/tree and apple fruit/tree, this paper carries on the correlation analysis of the ratio data with synchronization hyperspectral detection data, to determine the spectral features and sensitive spectrum wave band of the apple florescence and fruit. By means of the regression model through the sensitive band with fruit/tree, the volume of fruit will be better forecasted.The results show that:(1) The spectral features of apple florescence mainly presented as absorption of blue light and red light, reflection of green light, and the strong reflection of near-infrared between 750nm to 1300nm, in which the reflectivity up to 0.1 in visible light, and the reflectivity up to 0.35 in visible light. The change of apple florescence spectral features and the target information of apple flower/leaf and apple flower/tree present a good correlation, and it indicates that the sensitive wave band range is 400nm-530nm cyan light 570nm-700nm orange-red light, and the 760nm-1350nm nearly infrared of medium flower/tree of apple trees.(2) The spectral features of apple fruit period mainly presented as absorption of blue light and red light, reflection of green light, and the strong reflection of near-infrared between 750nm to 1300nm, and two reflection peak nearby 1650nm and 2200nm, in which the reflectivity up to 0.08 in visible light, and the reflectivity up to 0.6 in visible light. The results of correlation analysis show that the sensitive band is 435nm,670nm,700nm,940nm,1140nm and 1480nm.(3)We bulid difference vegetation index,ratio vegetation index and normalized difference vegetation index respectively use these sensitive bands, and filter the best spectral parameters through the analysis of the correlation between vegetation index with different ratio of fruit/tree of fruit trees, and then build the estimation model of fruit volume(ratio of fruit/tree). In this study we confirm the estimation model y=0.0086[NDVI(940,730)]2 1.0934NDVI(940,730)+0.3209 as the best model for the estimate of apple fruit volume. This study puts forward an effective way of the hyperspectral remote sensing with the combination of digital photos, preliminary proved the spectral characteristics of flowering and fruit apple trees, analysis and comparison the spectral features for flowering and fruit tree, carried on the estimation of the amount of fruit. This model provides a relatively fast estimation approach for the accurate estimate for the fruit volume of apple fruit petiod, and it provides a theoretical basis and technical support for the extracting apple's field information, nutrition diagnosis and apple production and management of real-time and informationization.