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An evaluation of the silicon spectral range for determination of nutrient content of grape vines.

机译：用于确定葡萄中营养成分的硅光谱范围的评估。

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摘要

The grape industry relies on in situ crop assessment to aid in the day-to-day and seasonal management of their crop. In the case of soil-plant chemistry interactions, there are six key nutrients of interest to viticulturists in the growing of wine grapes: nitrogen, potassium, phosphorous, magnesium, zinc, and boron. Traditional methods of determining the levels of these nutrients are through collection and chemical analysis of petiole samples from the grape vines themselves. In this study, however, we collected ground-level observations of the spectra of the grape vines using a hyperspectral spectroradiometer (0.4--2.5microm range; 1nm resampled spectral interval) at the same time that petioles samples were harvested. The data were collected for two different grape cultivars, both during bloom and veraison phenological stages to provide analytical variability, while also considering the impact of temporal/seasonal change. The data were interpolated to 1nm bandwidths, yielding a consistent 1nm spectral resolution before comparing it to the nutrient data collected. Spectral reflectance also was resampled to match the 10nm bands used by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS); this was done to assess the efficacy of nutrient modeling using a more standard, airborne system's spectral resolution. Our analysis was limited to the silicon photodiode range to increase the utility of the approach for wavelength-specific cameras (via spectral filters) in a low cost unmanned aerial vehicle (UAV) platform. Five different approaches were tested to fit the data to the nutrient data. These were: a narrow-band Normalized Difference Index (NDI) approach using a standard linear fit, step-wise linear regression (SLR) using the silicon range of wavelengths, SLR using the NDI that correlated highly with the nutrient data, SLR using the 1st derivative of the reflectance spectra, and SLR using continuum-removed spectra, applied over the red trough (560--750nm) spectral region. For 1nm reflectance data, these methods generated models for nutrient modeling using between 2--10 wavelengths, and associated coefficients of determination values ranging between R2 = 0.74-0.86 across the six nutrients. In the case of the 10nm resampled spectral data, model fits ranged between R2 = 0.61--0.93 across the six nutrients, using 2--18 unique wavelength bands. These results bode well for eventual non-destructive, accurate and precise assessment of vineyard nutrient status through the use of UAVs.
机译：葡萄行业依靠原地作物评估来帮助对其作物进行日常和季节性管理。在土壤-植物化学相互作用的情况下，葡萄栽培者在酿酒葡萄的生长中有六种重要的营养素：氮，钾，磷，镁，锌和硼。确定这些养分含量的传统方法是通过收集和化学分析葡萄藤本身的叶柄样品。然而，在这项研究中，我们在收获叶柄样品的同时，使用高光谱光谱仪（0.4--2.5μm范围; 1nm重采样的光谱间隔）收集了葡萄藤光谱的地面观测资料。收集了两个不同葡萄品种的数据，包括开花期和实物物候期，以提供分析变异性，同时还考虑了时间/季节变化的影响。在将数据与收集的营养数据进行比较之前，将数据插值到1nm带宽，产生一致的1nm光谱分辨率。还对光谱反射率进行了重新采样，以匹配机载可见和红外成像光谱仪（AVIRIS）使用的10nm波段；这样做是为了使用更标准的机载系统的光谱分辨率评估营养模型的功效。我们的分析仅限于硅光电二极管范围，以提高该方法在低成本无人机（UAV）平台中用于波长特定相机（通过光谱滤镜）的实用性。测试了五种不同的方法以使数据适合营养数据。它们是：使用标准线性拟合的窄带归一化差异指数（NDI）方法，使用硅波长范围的逐步线性回归（SLR），使用与养分数据高度相关的NDI的SLR，使用反射光谱的一阶导数，以及使用连续去除光谱的SLR应用于红色波谷（560--750nm）光谱区域。对于1nm反射率数据，这些方法使用2--10个波长之间的波长，以及六种营养素的相关测定值系数在R2 = 0.74-0.86之间生成了用于营养素建模的模型。在10nm重采样光谱数据的情况下，使用2--18独特的波长范围，对六种营养素的模型拟合范围在R2 = 0.61--0.93之间。这些结果预示着通过使用无人飞行器最终对葡萄园养分状况进行无损，准确和精确评估的预兆。

著录项

  • 作者

    Anderson, Grant W. F.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Remote sensing.;Agriculture.
  • 学位 M.S.
  • 年度 2016
  • 页码 152 p.
  • 总页数 152
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 公共建筑;
  • 关键词

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