针对水稻植株三维叶绿素(SPAD)信息获取的最优波段选择问题研究

实时准确地获取作物叶绿素含量的三维空间分布信息,是作物营养、栽培和育种等科学研究和生产领域密切关注的问题。该研究以水稻植株为研究对象,采用改造后的普通单反相机加载近红外滤光片的方法,多角度获取水稻植株的多光谱图像。基于不同波段不同通道的组合图像计算多种植被指数,将其结果与对应的实测SPAD值之间建立水稻植株叶绿素(SPAD)预测模型,并筛选出最优预测模型。研究结果表明,近红外760nm波段的R通道与可见光G通道构建的GNDVI植被指数,与实测SPAD值建立的二次函数预测模型能够很好地反演水稻植株叶绿素(SPAD)含量,其中,R~2=0.758,RMSE=1.532。在此基础上,利用多角度成像三维建模方法建立具备纹理信息的水稻三维模型,将最优预测模型应用于水稻综合纹理图,得到水稻叶绿素含量三维空间分布信息,从而实现水稻生长情况以及叶绿素养分分布状况的快速无损检测。

Research on Optimal Near-Infrared Band Selection of Chlorophyll (SPAD ) 3D Distribution about Rice Plant

Whether the chlorophyll 3D distribution of crop is obtained accurately really attracts attention of scientific research and production field ,such as crop nutrition ,cultivation and breeding .In this study ,the research object is the rice plant .The trans-formed ordinary SLR camera with different near infrared filters was used to acquire the multispectral images of rice plant in multi-view .Five kinds of vegetation indexes were calculated by combination image based on different bands and different chan-nels .Then the optimal rice plant chlorophyll (SPAD value) prediction model was built between vegetation index and measured SPAD value .The research results showed that the prediction model with the quadratic function between GNDVI vegetation index and measured SPAD value can analyze chlorophyll content (SPAD value) of rice plant well ,R2 =0.758 ,RMSE= 1.532 .The GNDVI vegetation index was constructed by the R channel of near-infrared 760nm band and the G channel of visible light band . On this basis ,the rice 3D model with texture information was built by multi-angle imaging 3D modeling method .Meanwhile ,the optimal prediction model was applied to the integrated texture map of rice ,and then the chlorophyll 3D distribution of rice was obtained .So rapid nondestructive detection of rice growth condition and chlorophyll nutrient situation can be realized .