Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density
Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density
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DOI：
10.1007/s11119-006-9027-4
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发表时间：
2007-04-01
影响因子：
6.2
通讯作者：
Muhammed, H. Harnid
中科院分区：
文献类型：
--
作者：
Larsolle, A.;Muhammed, H. Harnid
利用光谱反射率来估计作物状况是一种适合于开发用于特定地点农业应用的传感器的方法。在开发光谱分析方法时，了解不同作物参数对光谱反射率剖面的影响是很重要的。本报告的目的是提出和评估一个多变量的方法，客观的高光谱分析在检查不同部分的反射光谱是如何受到疾病的严重程度和地上植物密度。从两个田间试验的数据，1998年小麦真菌病害的严重程度评估和地上植物密度测量2003年。该分析方法包括两个步骤：预处理步骤，其中数据被归一化和分类步骤，用于估计作物变量。仅使用12%的数据作为训练数据，该方法的决定系数（R-2）为94.3%的疾病严重度数据和96.9%的植物密度数据。提出的高光谱分析方法也可以用于提取光谱特征的疾病的严重程度和植物密度使用的实验数据。通常，观察到两个数据集的两种类型的光谱特征，关于增加的疾病严重性和降低的植物密度，（1）绿色反射率峰的平坦化以及近红外区域中反射率的普遍降低，（2）在550 ~ 750 nm可见光区，近红外反射率平台的肩部减小，而反射率平台的肩部增大。
Using spectral reflectance to estimate crop status is a method suitable for developing sensors for site-specific agricultural applications. When developing spectral analysis methods, it is important to know the influence of different crop parameters on the spectral reflectance profile. The objective of this report was to present and evaluate a multivariate method for objective hyperspectral analysis in the examination of how different parts of the reflectance spectrum are affected by disease severity and above ground plant density. Data from two field experiments were used; fungal disease severity assessments in wheat 1998 and above ground plant density measurements 2003. The analysis method consisted of two steps: a preprocessing step where the data was normalized and a classification step for estimating the crop variable. Using only 12% of the data as training data, the method resulted in coefficients of determination (R-2) of 94.3% for the disease severity data and 96.9% for the plant density data. The hyperspectral analysis method presented could also be used to extract spectral signatures of disease severity and plant density using the experimental data. In general, two types of spectral signatures for both data sets, with respect to increasing disease severity and decreasing plant density, were observed (1) a flattening of the green reflectance peak together with a general decrease in reflectance in the near infrared region and, (2) a decrease of the shoulder of the near infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm.