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FT-NIR Spectroscopy technique based analysis and Prediction on soil nutrient content of Lychee orchard—A case study in Zhongluotan of Guangzhou, South China

机译:基于FT-NIR光谱技术的荔枝园土壤养分含量分析与预测-以广州中螺潭为例

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Precision farming with Geoinformatics is a new paradigm for agricultural production, especially in a developing country as China. Precision agriculture is an integrated application referring to the advanced 3S (remote sensing, GIS and GPS) technology and spectroscopy, and the key of this technology is to obtain the spatial and temporal information of the environmental factors such as soil nutrients, which has an influence on the growth of the Lychee crop and agricultural environment protection, and then the appropriate measurement in standard prescription can be adopted to realize "prescription farming" with efficiency for agricultural resource saving and environment protecting. Considering the actual problems and deficiencies of the present technology in the quick information extraction of Lychee soil nutrient, we systematically analyzed the relationships between the intelligent soil Fourier NIR spectrum information and Lychee orchard soil nutrient components such as nitrate (N), total phosphorus (P), potassium (K), organic matter (OM), calcium (Ca), and magnesium (Mg). The content of these components in the 15 in-situ soil samples of Zhongluotan, Guangzhou South China was analyzed against near infrared spectroscopy. The NIR spectral data of soil was treated by partial least square (PLS) analysis, and then the best dimension of analysis was obtained with multivariate linear regression. Upon this, the calibration model was established. The content of some components in Lychee orchard soil was predicted using the calibration model. It is shown that the result of Fourier NIR spectroscopy method was highly related with those of chemical analysis method. The regression coefficients between measured and predicted values of N, OM and pH were 0.93, 0.92, and 0.93, respectively. The correlation coefficient between measured and predicted P, K, Ca, and Mg were satisfactorily acceptable at 0.65, 0.74, 0.61 and 0.69, respectively. It showed that the Fourier NIR spectroscopy method is a good tool for soil nutrient prediction of Lychee garden, especially for soil N, OM and pH. The accuracy of the FT-NIR based PLS models for predict soil nutrient concentration was adequate to be used as a quick evaluation of nutrient composition of Lychee orchard soil for the precision agriculture application.
机译:地理信息学的精确农业是农业生产的新范例,在中国这样的发展中国家尤其如此。精准农业是结合先进的3S(遥感,GIS和GPS)技术和光谱学的综合应用,此技术的关键是获取诸如土壤养分等环境因素的时空信息,这对影响通过对荔枝作物的生长和农业环境保护的研究,可以采用标准处方中的适当计量方法来实现“处方农业”,从而有效地节约了农业资源,保护了环境。考虑到本技术在荔枝土壤养分快速信息提取中的实际问题和不足,我们系统地分析了智能土壤傅里叶近红外光谱信息与荔枝果园土壤养分成分如硝酸盐(N),总磷(P)之间的关系。 ),钾(K),有机物(OM),钙(Ca)和镁(Mg)。采用近红外光谱法对广州华中洛滩15个原位土壤样品中这些成分的含量进行了分析。通过偏最小二乘分析处理土壤的近红外光谱数据,然后通过多元线性回归获得最佳分析维数。基于此,建立校准模型。利用标定模型对荔枝果园土壤中某些成分的含量进行了预测。结果表明,傅里叶近红外光谱分析方法的结果与化学分析方法的结果高度相关。 N,OM和pH的测量值与预测值之间的回归系数分别为0.93、0.92和0.93。测得的和预测的P,K,Ca和Mg之间的相关系数分别令人满意,分别为0.65、0.74、0.61和0.69。结果表明,傅里叶近红外光谱法是预测荔枝园土壤养分的好工具,特别是对土壤氮,OM和pH值的预测。基于FT-NIR的PLS模型用于预测土壤养分浓度的准确性足以用作荔枝果园土壤养分组成的快速评估,以用于精确农业应用。

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