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A Method Based on Digital Image Analysis for Estimating Crop Canopy Parameters

机译:基于数字图像分析的作物冠层参数估计方法

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This research is attempted to estimate the biomass and leaf area index through identifying the cotton canpoy and background pixels accurately. Cotton canopy was captured in different growth period of cotton by using an Olympus C740 Ultra Zoom digital camera. A protocol mixed hue of HLS color space with R, G value was setup and multiple judgment process was designed to extract cotton canopy pixels from background noise. A computer program based on the protocol above was designed simultaneity. Relation between PGCV that derived from the designed program and cotton canopy biomass or LAI with different N application and in different growth stages suggested that PGCV could reliably evaluate both biomass and LAI, empirical statistical showed that a significantly high coefficient between PGCV and cotton canopy biomass or LAI reached (r=0.97, respectively). The results indicated that image analysis is a promising method for quick diagnosis of crop growth and development.
机译:本研究试图通过准确识别棉花冠层和背景像素来估计生物量和叶面积指数。使用Olympus C740 Ultra Zoom数码相机在棉花的不同生长期捕获了棉花冠层。建立了将HLS颜色空间与R,G值混合的协议,并设计了多种判断过程以从背景噪声中提取棉花冠层像素。同时设计了基于上述协议的计算机程序。所设计的程序得出的PGCV与不同氮素施用量和不同生育期的棉冠生物量或LAI之间的关系表明,PGCV可以可靠地评估生物量和LAI,经验统计表明,PGCV与棉冠生物量或棉花之间的系数显着较高LAI达到(分别为r = 0.97)。结果表明,图像分析是一种快速诊断作物生长发育的有前途的方法。

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