Centro para el Desarrollo de la Agricultura Sostenible, Instituto Valenciano de Investigaciones Agrarias (IVIA), CV-315, km 10.7, 46113 Moncada, Valencia, Spain.
Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Valencia, Spain.
Sensors (Basel). 2023 Jul 19;23(14):6530. doi: 10.3390/s23146530.
The nutritional diagnosis of crops is carried out through costly foliar ionomic analysis in laboratories. However, spectroscopy is a sensing technique that could replace these destructive analyses for monitoring nutritional status. This work aimed to develop a calibration model to predict the foliar concentrations of macro and micronutrients in citrus plantations based on rapid non-destructive spectral measurements. To this end, 592 'Clementina de Nules' citrus leaves were collected during several months of growth. In these foliar samples, the spectral absorbance (430-1040 nm) was measured using a portable spectrometer, and the foliar ionomics was determined by emission spectrometry (ICP-OES) for macro and micronutrients, and the Kjeldahl method to quantify N. Models based on partial least squares regression (PLS-R) were calibrated to predict the content of macro and micronutrients in the leaves. The determination coefficients obtained in the model test were between 0.31 and 0.69, the highest values being found for P, K, and B (0.60, 0.63, and 0.69, respectively). Furthermore, the important P, K, and B wavelengths were evaluated using the weighted regression coefficients (BW) obtained from the PLS-R model. The results showed that the selected wavelengths were all in the visible region (430-750 nm) related to foliage pigments. The results indicate that this technique is promising for rapid and non-destructive foliar macro and micronutrient prediction.
作物的营养诊断是在实验室中通过昂贵的叶面离子分析来进行的。然而，光谱学是一种传感技术，可以取代这些破坏性分析来监测营养状况。本工作旨在开发一种基于快速非破坏性光谱测量的校准模型，以预测柑橘种植园叶片中大量和微量元素的浓度。为此，在几个月的生长过程中收集了 592 个‘克莱门汀·德努莱’柑橘叶片。在这些叶片样本中，使用便携式分光光度计测量叶片的光谱吸收（430-1040nm），并通过发射光谱法（ICP-OES）测定叶片的离子组学，用于测定大量和微量元素，以及凯氏定氮法来量化 N。基于偏最小二乘回归（PLS-R）的模型被校准以预测叶片中大量和微量元素的含量。在模型测试中获得的决定系数在 0.31 到 0.69 之间，其中 P、K 和 B 的值最高（分别为 0.60、0.63 和 0.69）。此外，还使用从 PLS-R 模型获得的加权回归系数（BW）评估了重要的 P、K 和 B 波长。结果表明，所选波长都在与叶片色素有关的可见光区域（430-750nm）内。结果表明，该技术有望用于快速和非破坏性的叶片大量和微量元素预测。
