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首页> 美国卫生研究院文献>Scientific Reports >Monitoring ratio of carbon to nitrogen (C/N) in wheat and barley leaves by using spectral slope features with branch-and-bound algorithm
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Monitoring ratio of carbon to nitrogen (C/N) in wheat and barley leaves by using spectral slope features with branch-and-bound algorithm

机译：利用分枝定界算法利用光谱斜率特征监测小麦和大麦叶片碳氮比（C / N）

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Ratio of carbon to nitrogen concentration (C/N) that can illuminate metabolic status of C and N in crop leaves is one valuable indicator for crop nutrient diagnosis. This study explored the feasibility of using spectral slope features from hyperspectral measurements with Branch-and-Bound (BB) algorithm to monitor leaf C/N in wheat and barley. Experimental data from barley in 2010 and wheat in 2012 were collected and used. The analyses prove that leaf C/N is closely related to leaf N concentration (LNC), which implies that it is feasible to apply spectral technology to monitor leaf C/N in that LNC may have been effectivly estimated by hyperspectral measurements. The results also show that many spectral slope features proposed in this study exhibit the significant correlations with leaf C/N. The best slope feature could evaluate changes of leaf C/N well, with R2 of 0.63 for wheat, 0.68 for barley and 0.65 for both species combined, respectively. using BB algorithm with input of optiaml four slope features can improve the accuracy of leaf C/N estimations with R2 over 0.81. It is concluded that using the spectral slope new features with BB method appears very promising and potential for remotely monitoring leaf C/N in crops.
机译：能够阐明作物叶片中C和N代谢状态的碳氮浓度比（C / N）是作物营养诊断的重要指标。这项研究探讨了使用高光谱测量的光谱斜率特征和分枝定界（BB）算法来监测小麦和大麦叶片C / N的可行性。收集并使用了2010年大麦和2012年小麦的实验数据。分析表明，叶的C / N与叶的N浓度（LNC）密切相关，这意味着应用光谱技术监测叶的C / N是可行的，因为LNC可能已通过高光谱测量得到了有效的估计。结果还表明，本研究中提出的许多光谱斜率特征都与叶片C / N呈显着相关性。最佳的坡度特征可以很好地评估叶片的C / N变化，小麦的R 2 分别为0.63，大麦为0.68和两种物种的总和为0.65。结合使用BB算法和最佳输入四个斜率特征，可以提高叶子C / N估计的准确性，R 2 超过0.81。结论是，利用光谱斜率和BB方法的新功能似乎非常有前途，并且具有潜在的远程监测农作物叶片C / N的潜力。

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