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水稻氮素机器视觉诊断最佳叶位和位点的选择研

, PP. 179-183

Keywords: 水稻,氮素,机器视觉,叶位,位点,变异系数

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Abstract:

选用扫描仪获取水稻叶片的数字图像,通过比较第1和第3完全展开叶(L1和L2)颜色参量的空间分布,研究基于机器视觉技术的水稻氮素诊断的最佳叶位和位点选择。结果表明基于机器视觉的水稻氮素营养诊断是有理论依据的,能反映出叶片的营养状况;选择B、b、b/(r+g)、b/r、b/g作为最优颜色特征参量;比较颜色特征参量对应的变异系数CV,得到低氮处理的CV明显高于正常氮素水平,同时CV随着叶位的增加而减小;不同位点的CV其叶尖和叶基的变化幅度较为接近,不同位点间差异不显著。初步研究选择第3完全展开叶作为水稻无损氮素诊断的最佳叶位。

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