Estimation of Macro and Micronutrients in Persimmon (Diospyros kaki L.) cv. ‘Rojo Brillante’ Leaves through Vis-NIR Reflectance Spectroscopy (original) (raw)
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Agriculture
Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The average reflectance spectrum of each leaf was extracted, and foliar ionomic analysis was used as a reference method to determine the actual concentration of the nutrients in the leaves. Analyses were performed via emission spectrometry (ICP-OES) for macro- and micronutrients after microwave digestion and using the Kjeldahl method to quantify nitrogen. Partial least square regression (PLS-R) was used to predict the nutrient concentration based on spectral data from the leaf using actual values of each element as predictor variables. Several methods were used to pre-process the spectra, including Savitzky–Golay (SG) smoothing, standard normal variate (SNV) and first (1D) and second der...
Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy
Frontiers in Plant Science, 2015
Sufficient nutrient application is one of the most important factors in producing quality citrus fruits. One of the main guides in planning citrus fertilizer programs is by directly monitoring the plant nutrient content. However, this requires analysis of a large number of leaf samples using expensive and time-consuming chemical techniques. Over the last 5 years, it has been demonstrated that it is possible to quantitatively estimate certain nutritional elements in citrus leaves by using the spectral reflectance values, obtained by using near infrared reflectance spectroscopy (NIRS). This technique is rapid, non-destructive, cost-effective and environmentally friendly. Therefore, the estimation of macro and micronutrients in citrus leaves by this method would be beneficial in identifying the mineral status of the trees. However, to be used effectively NIRS must be evaluated against the standard techniques across different cultivars. In this study, NIRS spectral analysis, and subsequent nutrient estimations for N, K, Ca, Mg, B, Fe, Cu, Mn, and Zn concentration, were performed using 217 leaf samples from different citrus trees species. Partial least square regression and different pre-processing signal treatments were used to generate the best estimation against the current best practice techniques. It was verified a high proficiency in the estimation of N (Rv = 0.99) and Ca (Rv = 0.98) as well as achieving acceptable estimation for K, Mg, Fe, and Zn. However, no successful calibrations were obtained for the estimation of B, Cu, and Mn.
Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS)
Analytical and Bioanalytical Chemistry, 2016
Early diagnosis of specific chloride toxicity in persimmon trees requires the reliable and fast determination of the leaf chloride content, which is usually performed by means of a cumbersome, expensive and time-consuming wet analysis. A methodology has been developed in this study as an alternative to determine chloride in persimmon leaves using near-infrared spectroscopy (NIRS) in combination with multivariate calibration techniques. Based on a training dataset of 134 samples, a predictive model was developed from their NIR spectral data. For modelling, the partial least squares regression (PLSR) method was used. The best model was obtained with the first derivative of the apparent absorbance and using just 10 latent components. In the subsequent external validation carried out with 35 external data this model reached r 2 = 0.93, RMSE = 0.16 % and RPD = 3.6, with standard error of 0.026 % and bias of −0.05 %. From these results, the model based on NIR spectral readings can be used for speeding up the laboratory determination of chloride in persimmon leaves with only a modest loss of precision. The intermolecular interaction between chloride ions and the peptide bonds in leaf proteins through hydrogen bonding, i.e. N-H•••Cl, explains the ability for chloride determinations on the basis of NIR spectra.
Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy
Sensors
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, t...
Agronomy, 2022
This study aimed to evaluate the concentrations of the main macroelements in leaves and fruit grown following organic and conventional practices, and to relate them to physico-chemical parameters during commercial fruit harvests. Three samplings were carried out during fruit maturation. Nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) were determined in leaves and in two fruit flesh areas: basal and apical. Weight, color, firmness, soluble tannins (ST), and total soluble solids (TSS) were also evaluated in fruit. During the study period, the lowering leaf N concentration was accompanied by its increment in flesh. Leaf P and K lowered but did not imply changes in these concentrations in fruit. N, P, and K concentrations were higher in the apical area than in the basal flesh. No changes in Ca concentration occurred in leaf, but Ca translocation from the basal to the apical area was detected in fruit. Management affected the concentrations of leaf K and Mg ...
Computers and Electronics in Agriculture, 2020
Precise nitrogen (N) fertilization requires new indices of plants' nutritional status. Non-structural carbohydrates (NSC) are the energetic currency of plants and can, thus, serve as a physiological indicator for their condition. Nevertheless, only a few records exist about NSC composition and allocation in crops, and their relationship with N uptake and the current methods to detect NSC compositions in plants are cumbersome and expensive, which limits their use. The current work aimed to associate the nutritional status of almond trees with the carbohydrate compositions in their roots, branches, and leaves by a high-throughput technique. We found that low N availability forces trees to allocate carbohydrates to their roots. High N availability, on the other hand, promoted above-ground vegetative growth, and minimized carbohydrate storage in the leaves. These observations implied that carbohydrate distribution could, indeed, serve as an indication of the nutritional status of the trees. To measure NSC content efficiently, we attempted to quantify soluble carbohydrates and starch in dried and powdered tissues by visible-to-shortwave infrared (VIS-NIR-SWIR; 350-2500 nm) reflectance spectroscopy, which is an inexpensive, safe, and non-destructive technique. We applied several multivariate statistical models based on the spectral datasets, including partial least squares-regression (PLS-R) and discriminant analysis (PLS-DA) as supervised registration and classification models. PLS-DA results of the N gradient in the roots and leaves showed an overall accuracy of 94% and 98%, respectively. PLS-R model performances of soluble carbohydrates and starch improved, in terms of the coefficient of determination (R 2), if the leaf and root samples were integrated. Moreover, we found that the SWIR spectral region (1100-2500 nm) had unique reflectance features that revealed the carbohydrate composition and starch concentrations in the different plant tissues. The analyses also clustered the reflectance by tree part (root, branch, or leaf tissues) and N availability, forming a holistic model that can identify the nutritional status of trees. Conclusively, it is suggested that reflectance spectroscopy at the SWIR spectral region could guide precise fertilization by high-throughput identification of plants' seasonal metabolism. 30 mg g −1 dry weight (DW) of nitrogen (N) or potassium (K), or over 10 mg g −1 DW of phosphorus (P), even if they are exposed to extremely high mineral concentrations. Hence, up to 50% of the current application of synthetic fertilizers will not translate to improved yields (Lassaletta et al., 2014). For this reason, among others, N fertilization is associated with large scale contamination of air, soil, and water resources (Compton et al., 2011). Excessive N fertilization could also interfere with crop productivity-eventually limiting further mineral uptake-and intensify contaminating runoffs (Sperling et al., 2019). Such conditions would produce visual indicators of overuse, but they will remain species-specific. Essentially, there is an acute need for new indicators, especially ones that will identify excessive mineral
The prediction of iron contents in orchards using VNIR spectroscopy
TURKISH JOURNAL OF AGRICULTURE AND FORESTRY, 2015
Introduction Traditional chemical analysis methods are typically timeconsuming. Therefore, visible/near-infrared (VNIR) reflectance spectroscopy has been increasingly used for plant analysis and nutrient deficiencies determination. This technique is rapid, nondestructive, and cost effective, requires minimal sample preparation, can be used in situ, does not involve hazardous chemicals, and, importantly, can detect several plant stresses from a single scan. The VNIR spectra can be used to determine sensitive organic composition (Viscarra Rossel et al., 2006). The conventional chemical analysis methods are usually timeconsuming. Therefore, near-infrared spectroscopy, a fast, nondestructive technique, has been widely used for quantitative analysis (Lillhonga and Gelady, 2011; Cao and Zhan, 2014). Plant nutrient element stress is one of the important abiotic stress factors that has long been investigated by researchers. Such stresses can occur in the case of deficiency or excess of mineral substances (Abadia et al., 2011). Plants absorb, reflect, emit, and distribute radiation coming from any source. Akin to any object, plants have a typical reflectance value. The energy released by the bonds between organic atoms such as-CH,-OH,-NH, C=O, and-SH in plant compositions is absorbed in the infrared (IR) region (Chang et al., 2001; Pasquini, 2003). Real-time spectral reflectance measurements in flora or intact leafs also provide estimations of biochemical composition of plants (
Espacio Tiempo y Forma. Serie VI, Geografía, 2019
The effect of anomalies of foliar mineral nutrients on the nutritional behavior of apple crops has been evaluated using parameters obtained by means of remote sensing techniques. Twenty-five plots in commercial orchards were selected in the five most important municipalities of Chihuahua State, Mexico in which the main nutritional parameters were measured (N, P, K, Ca, Mg, Fe, Zn, Mn, Cu, and B). Important deficiencies of these nutrients were detected in 88% of the analyzed crops. These deficiencies showed significant correlation with the spectral data (SPOT5) and with the NDVI elaborated from these data. The mathematical models obtained showed high determination coefficients for most of the mineral elements; concretely, nitrogen and calcium presented the best results (0.80 and 0.76, respectively).ResumenSe ha evaluado el efecto en las anomalías de los nutrientes minerals foliares en el comportamiento nutricional de los cultivos de manzanos utilizando parámetros obtenidos mediante t...
Computers and Electronics in Agriculture, 2018
Nuts are nutritionally valuable for a healthy diet but can be prone to rancidity due to their high unsaturated fat content. Nutrient content of nuts is an important component of their health benefits but measuring both rancidity and nutrient content of nuts is laborious, tedious and expensive. Hyperspectral imaging has been used to predict chemical composition of plant parts. This technique has the potential to rapidly predict chemical composition of nuts, including rancidity. Hence, this study explored to what extent hyperspectral imaging (400-1000 nm) could predict chemical components of Canarium indicum nuts. Partial least squares regression (PLSR) models were developed to predict kernel rancidity using peroxide value (PV) for two different batches of kernels, and macro-and micronutrients of kernels using the spectra of the samples obtained from hyperspectral images. The models provided acceptable prediction abilities with strong coefficients of determination (R 2) and ratios of prediction to deviation (RPD) of the test set for PV, first batch (R 2 = 0.72; RPD = 1.66), PV, second batch (R 2 = 0.81; RPD = 2.30), total nitrogen (R 2 = 0.80; RPD = 1.58), iron (R 2 = 0.75; RPD = 1.46), potassium (R 2 = 0.51; RPD = 0.94), magnesium (R 2 = 0.81; RPD = 2.04), manganese (R 2 = 0.71; RPD = 1.84), sulphur (R 2 = 0.76; RPD = 1.84) and zinc (R 2 = 0.62; RPD = 1.37) using selected wavelengths. This study indicated that visible-near infrared (VNIR) hyperspectral imaging has the potential to be used for prediction of chemical components of C. indicum nuts without the need for destructive analysis. This technique has potential to be used to predict chemical components in other nuts.