The aim of this study was to predict the critical nitrogen (N) content in peach trees using spectrometric measurements. A nutrient-controlled hydroponics experiment was designed for this purpose. Peach saplings were grown under three N conditions: deficient, sufficient, and excessive. The reflectance values of a plant leaves were measured using a handheld field spectroradiometer fitted with a plant probe. The N contents of leaves were determined in the laboratory and Gaussian mixture discriminant analysis (GMDA) was used to estimate N levels in the leaves from reflectance values. The N levels were categorized for each of the three different N conditions. The wavelengths at 425 nm, 574 nm, 696 nm, and 700 nm were found to be diagnostic of the different N levels. The model developed here classified the experimental plants with high accuracy for NDeficient, 89.28%; NSufficient, 96.30%; and NExcess, 71.42% with 85.71% coefficients. The reliability of the model was also tested under field conditions using 96 peach trees representing the three different N status. Leaves were analyzed by reflectance at 425 nm, 574 nm, 696 nm, and 700 nm, which functioned in real N, percentage classes determined based on the laboratory analyses of the orchard samples, and the data were categorized as NDeficient, NSufficient, and NExcess with a similarity ratio of 77.78%, 80%, and 67.74%, respectively with the general correct classification rate of 75%. The study findings showed that the model developed using hyperspectral reflectance data can discriminate different N nutritional status in plants with an accuracy of ≥70% and can be applied under field conditions. The results of this research provide a new perspective for future studies by showing that GMDA with hyperspectral remote sensing may be useful for the classification of different plant nutrient contents.

中文翻译：

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用可见-近红外光谱混合分析法估算桃园临界氮含量

本研究的目的是使用光谱测量预测桃树中的临界氮 (N) 含量。为此目的设计了一项营养控制的水培实验。桃树苗在三种氮条件下生长：不足、充足和过量。使用装有植物探针的手持式现场光谱仪测量植物叶子的反射率值。叶片的 N 含量在实验室中测定，高斯混合判别分析 (GMDA) 用于根据反射率值估算叶片中的 N 含量。N 水平针对三种不同的 N 条件中的每一种进行分类。发现 425 nm、574 nm、696 nm 和 700 nm 的波长可诊断不同的 N 水平。这里开发的模型对实验植物进行分类，NDeficient 的准确率高达 89.28%；NS充足，96.30%；和 NExcess，71.42%，系数为 85.71%。还在田间条件下使用代表三种不同氮状态的 96 棵桃树测试了模型的可靠性。通过在 425 nm、574 nm、696 nm 和 700 nm 处的反射率分析叶子，这些反射率在实际 N 中起作用，根据果园样品的实验室分析确定百分比等级，并将数据分类为 NDeficient、NSufficient 和 NExcess相似度分别为77.78%、80%和67.74%，一般正确分类率为75%。研究结果表明，利用高光谱反射率数据开发的模型能够以≥70%的准确率区分植物中不同的氮营养状况，并且可以在田间条件下应用。这项研究的结果表明，具有高光谱遥感的 GMDA 可能有助于对不同植物营养成分进行分类，为未来的研究提供了新的视角。