scholarly journals Estimating Soil Properties and Nutrients by Visible and Infrared Diffuse Reflectance Spectroscopy to Characterize Vineyards

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1895
Author(s):  
José Ramón Rodríguez-Pérez ◽  
Víctor Marcelo ◽  
Dimas Pereira-Obaya ◽  
Marta García-Fernández ◽  
Enoc Sanz-Ablanedo
Keyword(s):  
Soil Properties ◽  
Diffuse Reflectance Spectroscopy ◽  
Full Range ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Western Spain ◽  
Shortwave Infrared

Visible, near, and shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy, a cost-effective and rapid means of characterizing soils, was used to predict soil sample properties for four vineyards (central and north-western Spain). Sieved and air-dried samples were measured using a portable spectroradiometer (350–2500 nm) and compared for pistol grip (PG) versus contact probe (CP) setups. Raw data processed using standard normal variate (SVN) and detrending transformation (DT) were grouped into four subsets (VIS: 350–700 nm; NIR: 701–1000 nm; SWIR: 1001–2500 nm; and full range: 350–2500 nm) in order to identify the most suitable range for determining soil characteristics. The performance of partial least squares regression (PLSR) models in predicting soil properties from reflectance spectra was evaluated by cross-validation. The four spectral subsets and transformed reflectances for each setup were used as PLSR predictor variables. The best performing PLSR models were obtained for pH, electrical conductivity, and phosphorous (R2 values above 0.92), while models for sand, nitrogen, and potassium showed moderately good performances (R2 values between 0.69 and 0.77). The SWIR subset and SVN + DT processing yielded the best PLSR models for both the PG and CP setups. VIS-NIR-SWIR reflectance spectroscopy shows promise as a technique for characterizing vineyard soils for precision viticulture purposes. Further studies will be carried out to corroborate our findings.

scholarly journals PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Scatter Correction ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal ◽  
Cucumis Melo L

Penelitian ini bertujuan memprediksi total padatan terlarut buah melon golden menggunakan Vis-SWNIRS dan analisis multivariat. 82 sampel buah melon golden dipanen untuk dianalisis di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Nirvana AG410 spectrometer dengan rentang panjang gelombang 300 sampai 1050 nm digunakan untuk pengambilan data spektra pada sampel buah melon utuh. Metode koreksi spektra yang digunakan yaitu standard normal variate (SNV), multiplicative scatter correction (MSC), dan orthogonal signal correction (OSC). Pemodelan kalibrasi dilakukan menggunakan partial least squares regression (PLSR). Hasil penelitian menunjukkan bahwa penggunaan metode koreksi spektra OSC menampikan model kalibrasi terbaik dibandingkan spektra original dan 2 spektra lainnya yang telah dikoreksi. Koefisien determinasi pada spektra OSC memperlihatkan nilai R2 tertinggi yaitu 0.99, disamping itu nilai ratio performance to deviation (RPD) yang diperoleh sebesar 3.40. Hal ini membuktikan bahwa total padatan terlarut buah melon golden dapat diprediksi dengan akurasi yang tinggi menggunakan Vis-SWNIRS dan analisis multivariat.

scholarly journals Determination of Total Flavonoids for Paris Polyphylla Var. Yunnanensis in Different Geographical Origins Using UV and FT-IR Spectroscopy

2019 ◽  
Vol 102 (2) ◽  
pp. 457-464
Author(s):  
Yuangui Yang ◽  
Yanli Zhao ◽  
Zhitian Zuo ◽  
Yuanzhong Wang
Keyword(s):  
Uv Spectrophotometry ◽  
Total Flavonoids ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Paris Polyphylla ◽  
Orthogonal Signal Correction ◽  
Relevant Variables ◽  
Standard Normal ◽  
Ft Ir

Abstract Background: Paris polyphylla var. Yunnanensis (PPY) is used in the clinical treatment of tumors, hemorrhages, and anthelmintic. Objective: The aim of this study is to determine total flavonoids of PPY in the Yunnan and Guizhou Provinces, China. Methods: In this study, total flavonoids were determined by UV spectrophotometry at first. Then, Fourier transform mid-infrared (FT-IR) based on various pretreatments include standard normal variate (SNV), first derivative (FD), second derivative (SD), Savitzky-Golay (SG), and orthogonal signal correction (OSC) were investigated. In addition, several relevant variables were screened by competitive adaptive reweighted sampling (CARS). The contentof total flavonoids and selected variables of FT-IRwere used to establish a partial least squares regression for PPY in different regions. Results: The results indicated that CARS was an effective method for decreasing the variable of thedatabase and improving the prediction of the model.FT-IR with pretreatment SNV + OSC + FD + SG had thebest performance, with R2 > 0.9 and residual predictive deviation = 3.3515, which could be used forthe predictive model of total flavonoids. Conclusions: Those results would provide a fast and robust strategy for the determination of total flavonoids of PPY in different geographical origin. Highlights: Various pretreatments, including SNV, FD, SD, SG, and OSC, were compared; several relevant variables were selected by CARS; and the content of total flavonoids and selected variable were used to establish a partial leastsquares regression for PPY in different regions.

scholarly journals Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1712
Author(s):  
Antonio Leone ◽  
Guido Leone ◽  
Natalia Leone ◽  
Ciro Galeone ◽  
Eleonora Grilli ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Water ◽  
Water Retention ◽  
Southern Italy ◽  
Multivariate Calibration ◽  
Diffuse Reflectance Spectroscopy ◽  
Soil Analysis ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Soil Water Retention

In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

scholarly journals Association of spectroscopically determined leaf nutrition related traits and breeding selection in Sassafras tzumu

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jun Liu ◽  
Yang Sun ◽  
Wenjian Liu ◽  
Zifeng Tan ◽  
Jingmin Jiang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Plant Traits ◽  
Leaf Traits ◽  
Breeding Program ◽  
Least Squares Regression ◽  
Living Tree ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Breeding Selection ◽  
Leaf Nutrition

Abstract Background Plant traits related to nutrition have an influential role in tree growth, tree production and nutrient cycling. Therefore, the breeding program should consider the genetics of the traits. However, the measurement methods could seriously affect the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including anthocyanins (ANTH), flavonoids (FLAV) and nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Fresh leaves of Sassafras tzumu were selected for spectral collection and ANTH, FLAV and NBI concentrations measurement by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal model selection. Each trait model was simulated 200 times for error estimation. Results The standard normal variate (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance models. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10%, followed by FLAV and NBI model (mean R2 of 0.58, mean RMSE of 0.11% and mean R2 of 0.44, mean RMSE of 0.04%). High heritability was found for ANTH, FLAV and NBI with h2 of 0.78, 0.58 and 0.61 respectively. It shows that it is beneficial and possible for breeding selection to the improvement of leaf nutrition traits. Conclusions Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

scholarly journals Reflectance Spectroscopy for Non-Destructive Measurement and Genetic Analysis of Amounts and Types of Epicuticular Waxes on Onion Leaves

Molecules ◽  
2020 ◽  
Vol 25 (15) ◽  
pp. 3454 ◽  
Author(s):  
Eduardo D. Munaiz ◽  
Philip A. Townsend ◽  
Michael J. Havey
Keyword(s):  
Prediction Models ◽  
Full Range ◽  
Reflectance Spectroscopy ◽  
Thrips Tabaci ◽  
Gas Chromatography Mass Spectrometry ◽  
Spectral Features ◽  
Epicuticular Waxes ◽  
Least Squares Regression ◽  
Destructive Measurement ◽  
Non Destructive

Epicuticular waxes on the surface of plant leaves are important for the tolerance to abiotic stresses and plant–parasite interactions. In the onion (Allium cepa L.), the variation for the amounts and types of epicuticular waxes is significantly associated with less feeding damage by the insect Thrips tabaci (thrips). Epicuticular wax profiles are measured using used gas chromatography mass spectrometry (GCMS), which is a labor intensive and relatively expensive approach. Biochemical spectroscopy is a non-destructive tool for measurement and analysis of physiological and chemical features of plants. This study used GCMS and full-range biochemical spectroscopy to characterize epicuticular waxes on seven onion accessions with visually glossy (low wax), semi-glossy (intermediate wax), or waxy (copious wax) foliage, as well as a segregating family from the cross of glossy and waxy onions. In agreement with previous studies, GCMS revealed that the three main waxes on the leaves of a wild type waxy onion were the ketone hentriacontanone-16 (H16) and fatty alcohols octacosanol-1 (Oct) and triacontanol-1 (Tri). The glossy cultivar “Odourless Greenleaf” had a unique phenotype with essentially no H16 and Tri and higher amounts of Oct and the fatty alcohol hexacosanol-1 (Hex). Hyperspectral reflectance profiles were measured on leaves of the onion accessions and segregating family, and partial least-squares regression (PLSR) was utilized to generate a spectral coefficient for every wavelength and prediction models for the amounts of the three major wax components. PLSR predictions were robust with independent validation coefficients of determination at 0.72, 0.70, and 0.42 for H16, Oct, and Tri, respectively. The predicted amounts of H16, Oct, and Tri are the result of an additive effect of multiple spectral features of different intensities. The variation of reflectance for H16, Oct, and Tri revealed unique spectral features at 2259 nm, 645 nm, and 730 nm, respectively. Reflectance spectroscopy successfully revealed a major quantitative trait locus (QTL) for amounts of H16, Oct, and Tri in the segregating family, agreeing with previous genetic studies. This study demonstrates that hyperspectral signatures can be used for non-destructive measurement of major waxes on onion leaves as a basis for rapid plant assessment in support of developing thrips-resistant onions.

Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

2016 ◽  
Vol 96 (4) ◽  
pp. 372-385 ◽  
Author(s):  
Zhenzhen Xiao ◽  
Yi Li ◽  
Hao Feng
Keyword(s):  
Soil Properties ◽  
Diffuse Reflectance ◽  
Model Performance ◽  
Sodium Adsorption Ratio ◽  
Support Vector ◽  
Least Squares Regression ◽  
Spectral Processing ◽  
Transformation Techniques ◽  
Intrinsic Factors ◽  
Standard Normal

Spectral analysis is a useful tool for the rapid and accurate prediction of soil properties. Our objective is to select the best model for predicting the three soil cation concentrations ([Na+], [Mg2+], and [Ca2+]) and sodium adsorption ratio (SAR). Three methods were applied, i.e., stepwise multiple linear regression (SMLR), partial least-squares regression (PLSR), and support vector machine (SVM). Estimation models for four soil properties were developed using three different spectral processing and transformation techniques, i.e., reflectance (Re), logarithm of reciprocal Re (LR), and standard normal variable of Re (SNV) were used. A total of 36 models were established. Of these, 27 models for [Na+], [Mg2+], and [Ca2+] were not applicable for subsequent prediction, because the coefficients of determination (R2) were not high (0.224–0.689), and their relative percent deviations (RPD) were all smaller than the 1.4 threshold. However, the models for SAR~R using PLSR (R2 = 0.728 for calibration and 0.661 for validation, RPD = 1.43), SAR~LR using SVM (R2 = 0.791 for calibration and 0.712 for validation, RPD = 1.81), and SAR~SNV using SVM (R2 = 0.878 for calibration and 0.814 for validation, RPD = 2.13) were valid for further prediction. Finally, SAR~SNV using SVM was selected as the best model. There are intrinsic factors resulting in an unsatisfied model performance.

scholarly journals Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1011 ◽  
Author(s):  
Xiaoshuai Pei ◽  
Kenneth Sudduth ◽  
Kristen Veum ◽  
Minzan Li
Keyword(s):  
Soil Properties ◽  
Soil Profile ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Chemical Properties ◽  
Estimation Accuracy ◽  
Soil Core ◽  
Least Squares Regression ◽  
Calibration Methods

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

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