Dissimilarity of parameterized maps generated in geostatistics and the assumption of an automatic software model in precision agriculture

Geostatistics is the main technique used to efficiently determine spatial variability. The objective of this study was to evaluate the applicability of the principles of geostatistics in the use of semivariograms elaborated through parametric monitoring and the assumption automatically made by software in the map preparation of soil chemical attributes. Available phosphorus (P), potassium (K+), calcium (Ca2+), magnesium (Mg2+), base saturation (V%), sulfur (SO42-), and pH were compared from the soil chemical attributes of 60 samples of a Typical Oxisol collected at a 0-20 cm depth and a distance of 300 m between the points. The maps were compared using error matrices and evaluated by the Global Accuracy (GA), Kappa (K), and Tau (T) indexes. The parameterized semivariograms and the automatic software model assumption did not present a high coincidence for the available P and Mg2+, making it necessary to adjust the semivariogram variables in the spatial analysis as a function of the outliers, sum of squares of residuals, coefficient of determination, and cross-validation to better represent the variability of the data and thus avoid distortions of the sample point range that would affect the adequate representativeness of the attributes, which contrasts with the automatic model generated by the software.

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Quantitative Breast Density in Contrast-Enhanced Mammography

Journal of Clinical Medicine ◽

10.3390/jcm10153309 ◽

2021 ◽

Vol 10 (15) ◽

pp. 3309

Author(s):

Gisella Gennaro ◽

Melissa L. Hill ◽

Elisabetta Bezzon ◽

Francesca Caumo

Keyword(s):

Breast Density ◽

Potential Role ◽

Multiple Correlation Coefficient ◽

Low Energy ◽

Coefficient Of Determination ◽

Contrast Enhanced ◽

Increased Risk ◽

Significant Difference ◽

Automatic Software ◽

The Relationship

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms.

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Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

Remote Sensing ◽

10.3390/rs13112121 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2121

Author(s):

Changsuk Lee ◽

Kyunghwa Lee ◽

Sangmin Kim ◽

Jinhyeok Yu ◽

Seungtaek Jeong ◽

...

Keyword(s):

Cross Validation ◽

Ground Level ◽

Reanalysis Data ◽

Training Dataset ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Air Quality Model ◽

Quality Model ◽

Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

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Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES

Agronomy ◽

10.3390/agronomy11050938 ◽

2021 ◽

Vol 11 (5) ◽

pp. 938

Author(s):

Ladislav Menšík ◽

Lukáš Hlisnikovský ◽

Pavel Nerušil ◽

Eva Kunzová

Keyword(s):

Precision Agriculture ◽

Alluvial Soil ◽

Coefficient Of Determination ◽

Fast Method ◽

Agricultural Practice ◽

Regression Equations ◽

Icp Oes ◽

Risk Elements ◽

The Relationship

The aim of the study was to compare the concentrations of risk elements (As, Cu, Mn, Ni, Pb, Zn) in alluvial soil, which were measured by a portable X-ray fluorescence analyser (pXRF) in situ (FIELD) and in the laboratory (LABORATORY). Subsequently, regression equations were developed for individual elements through the method of construction of the regression model, which compare the results of pXRF with classical laboratory analysis (ICP-OES). The accuracy of the measurement, expressed by the coefficient of determination (R2), was as follows in the case of FIELD–ICP-OES: Pb (0.96), Zn (0.92), As (0.72), Mn (0.63), Cu (0.31) and Ni (0.01). In the case of LABORATORY–ICP-OES, the coefficients had values: Pb (0.99), Zn (0.98), Cu and Mn (0.89), As (0.88), Ni (0.81). A higher dependence of the relationship was recorded between LABORATORY–ICP-OES than between FIELD–ICP-OES. An excellent relationship was recorded for the elements Pb and Zn, both for FIELD and LABORATORY (R2 higher than 0.90). The elements Cu, Mn and As have a worse tightness in the relationship; however, the results of the model have shown its applicability for common use, e.g., in agricultural practice or in monitoring the quality of the environment. Based on our results, we can say that pXRF instruments can provide highly accurate results for the concentration of risk elements in the soil in real time for some elements and meet the principle of precision agriculture: an efficient, accurate and fast method of analysis.

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Dissolved and particulate nutrient transport dynamics of a small Irish catchment: the River Owenabue

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2191-2014 ◽

2014 ◽

Vol 18 (6) ◽

pp. 2191-2200 ◽

Cited By ~ 4

Author(s):

S. T. Harrington ◽

J. R. Harrington

Keyword(s):

Suspended Sediment ◽

Water Discharge ◽

Nutrient Transport ◽

Sediment Concentration ◽

Coefficient Of Determination ◽

Available Phosphorus ◽

Nitrogen And Phosphorus ◽

Particulate Nitrogen ◽

Monitoring Period ◽

Nitrogen Concentrations

Abstract. The objective of this research was to investigate the relationship between water and sediment discharge on the transport of nutrients: nitrogen and phosphorus. Water discharge, suspended sediment concentration and dissolved and particulate forms of nitrogen and phosphorus were monitored on the 105 km2 River Owenabue catchment in Ireland. Water discharge was found to have an influence on both particulate and dissolved nutrient transport, but more so for particulate nutrients. The particulate portion of N and P in collected samples was found to be 24 and 39%, respectively. Increased particulate nitrogen concentrations were found at the onset of high discharge events, but did not correlate well to discharge. High concentrations of phosphorus were associated with increased discharge rates and the coefficient of determination (r2) between most forms of phosphorus and both discharge and suspended sediment concentrations were observed to be greater than 0.5. The mean TN yield is 4004 kg km−2 yr−1 for the full 29-month monitoring period with a mean PN yield of 982 kg km−2 yr−1, 25% of the TN yield with the contribution to the yield of PN and PP estimated to be 25 and 53% respectively. These yields represent a PN and PP contribution to the suspended sediment load of 5.6 and 0.28% respectively for the monitoring period. While total nitrogen and total phosphorus levels were similar to other European catchments, levels of bio-available phosphorus were elevated indicating a potential risk of eutrophication within the river.

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Using Cross-Validation to Evaluate CERES-Maize Yield Simulations within a Decision Support System for Precision Agriculture

Transactions of the ASABE ◽

10.13031/2013.23605 ◽

2007 ◽

Vol 50 (4) ◽

pp. 1467-1479 ◽

Cited By ~ 22

Author(s):

K. R. Thorp ◽

W. D. Batchelor ◽

J. O. Paz ◽

A. L. Kaleita ◽

K. C. DeJonge

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Precision Agriculture ◽

Cross Validation ◽

Maize Yield

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Research on Crude Protein Contents in Medicago Sativa Hay Harvest During 2008-2009 Using FT-NIR Spectrometry

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca Agriculture ◽

10.15835/buasvmcn-agr:6426 ◽

2011 ◽

Vol 68 (1) ◽

Author(s):

Laura DALE ◽

Ioan ROTAR ◽

Vasile FLORIAN ◽

Roxana VIDICAN ◽

André THEWIS ◽

...

Keyword(s):

Medicago Sativa ◽

Protein Content ◽

Crude Protein ◽

Nutritive Value ◽

Cross Validation ◽

External Validation ◽

Flowering Plant ◽

Coefficient Of Determination ◽

Research Station ◽

Crude Protein Content

Medicago sativa or alfalfa is a flowering plant that belongs to Pea Family that is widely grown throughout the world as forage for cattle, and is most often harvested as hay. Usually, alfalfa has the highest nutritive value of all common hay crops. This work aims to highlight a way for direct, non-destructive analysis of crude protein content in alfalfa hays. The primary objective was to build a model for crude protein calibration for alfalfa based on FT-NIR spectroscopy. The samples for analysis were collected over two experimental years (2008-2009) from field trials from the research station– Agricultural Development, Cojocna. In order to construct the model, reference values are needed; for this reason, the crude protein content was determined using the classical Kjeldahl method (Kjeltec Auto Analyser, Tecator). The values for crude protein ranged from 12.63% to 19.12% on the dry matter basis. The regression model’s construction was based on Partial Least Squares (PLS) calculated with the SIMPLS algorithm, using different pre-processing techniques and leave-one-out cross validation. Calibration of the two years together drove to a coefficient of determination for cross validation, R2 of 0.965. The robustness of the model was confirmed by applying it to independent samples (external validation) where the coefficient of determination was R2 = 0.977, RMSEP = 0.8. The results obtained indicated that NIRS can be used to determine crude protein, which could be used as criteria for quality control of alfalfa hays.

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Application of FTIR Spectroscopy and Chemometrics for Halal Authentication of Beef Meatball Adulterated with Dog Meat

Indonesian Journal of Chemistry ◽

10.22146/ijc.27159 ◽

2018 ◽

Vol 18 (2) ◽

pp. 376 ◽

Cited By ~ 2

Author(s):

Wiranti Sri Rahayu ◽

Abdul Rohman ◽

Sudibyo Martono ◽

Sudjadi Sudjadi

Keyword(s):

Ftir Spectroscopy ◽

Reliable Method ◽

Cross Validation ◽

Partial Least Square ◽

Least Square ◽

Coefficient Of Determination ◽

Calibration Model ◽

Mean Square ◽

Folch Method ◽

Halal Authentication

Beef meatball is one of the favorite meat-based food products among Indonesian community. Currently, beef is very expensive in Indonesian market compared to other common meat types such as chicken and lamb. This situation has intrigued some unethical meatball producers to replace or adulterate beef with lower priced-meat like dog meat. The objective of this study was to evaluate the capability of FTIR spectroscopy combined with chemometrics for identification and quantification of dog meat (DM) in beef meatball (BM). Meatball samples were prepared by adding DM into BM ingredients in the range of 0–100% wt/wt and were subjected to extraction using Folch method. Lipid extracts obtained from the samples were scanned using FTIR spectrophotometer at 4000–650 cm-1. Partial least square (PLS) calibration was used to quantify DM in the meatball. The results showed that combined frequency regions of 1782–1623 cm-1 and 1485-659 cm-1 using detrending treatment gave optimum prediction of DM in BM. Coefficient of determination (R2) for correlation between the actual value of DM and FTIR predicted value was 0.993 in calibration model and 0.995 in validation model. The root mean square error of calibration (RMSEC) and standard error of cross validation (SECV) were 1.63% and 2.68%, respectively. FTIR spectroscopy combined with multivariate analysis can serve as an accurate and reliable method for analysis of DM in meatball.

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Development of multistage 10-m shuttle run test for VO2max estimation in healthy adults

10.31083/jomh.2021.066 ◽

2021 ◽

Keyword(s):

Cross Validation ◽

Age Groups ◽

Healthy Adults ◽

Coefficient Of Determination ◽

Validation Test ◽

Uniform Distributions ◽

Gender And Age ◽

Run Test ◽

Shuttle Run ◽

Spatial Limitation

Background and objective: The disadvantage of the traditional 20-m multistage shuttle run test (MST) is that it requires a long space for measurements and does not include various age groups to develop the test. Therefore, we developed a new MST to improve the spatial limitation by reducing the measurement to a 10-m distance and to resolve the bias via uniform distributions of gender and age. Material and methods: Study subjects included 120 healthy adults (60 males and 60 females) aged 20 to 50 years. All subjects performed a graded maximal exercise test (GXT) and a 10-m MST at ﬁve-day intervals. We developed a regression model using 70% of the subject's data and performed a cross-validation test using 30% of the data. Results: The male regression model's coeﬃcient of determination (R2) was 58.8%, and the standard error of estimation (SEE) was 4.17 mL/kg/min. The female regression model's R2 was 69.2%, and the SEE was 3.39 mL/kg/min. The 10-m MST showed a high correlation with GXT on the VO2max (males: 0.816; females: 0.821). In the cross-validation test for the developed regression models, the male's SEE was 4.38 mL/kg/min, and the female's SEE was 4.56 mL/kg/min. Conclusion: Thus, the 10-m MST is an accurate and valid method for estimating the VO2max. Therefore, the 10-m MST developed by us can be used when the existing 20-m MST cannot be used due to spatial limitations and can be applied to both men and women in their 20s and 50s.

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A simple approach to estimate coastal soil salinity using digital camera images

Soil Research ◽

10.1071/sr20009 ◽

2020 ◽

Vol 58 (8) ◽

pp. 737

Author(s):

Lu Xu ◽

Raphael A. Viscarra Rossel ◽

Juhwan Lee ◽

Zhichun Wang ◽

Hongyuan Ma

Keyword(s):

Soil Salinity ◽

Precision Agriculture ◽

Mean Squared Error ◽

Rapid Development ◽

Salt Content ◽

Digital Camera ◽

Soil Surface ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

High Concentration

Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and sampled the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.

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Using Raman Spectroscopy as a Fast Tool to Classify and Analyze Bulgarian Wines—A Feasibility Study

Molecules ◽

10.3390/molecules25010170 ◽

2019 ◽

Vol 25 (1) ◽

pp. 170 ◽

Cited By ~ 1

Author(s):

Vera Deneva ◽

Ivan Bakardzhiyski ◽

Krasimir Bambalov ◽

Daniela Antonova ◽

Diana Tsobanova ◽

...

Keyword(s):

Raman Spectroscopy ◽

Phenolic Compounds ◽

Cross Validation ◽

Geographic Origin ◽

Coefficient Of Determination ◽

Total Phenolic ◽

Total Phenolic Compounds ◽

Calibration Models ◽

Rich Information ◽

Fast Classification

Raman spectroscopy, being able to provide rich information about the chemical composition of the sample, is gaining an increasing interest in the applications of food. Raman spectroscopy was used to analyze a set of wine samples (red and white) sourced from rarely studied traditional Bulgarian wines. One of the objectives of this study was to attempt the fast classification of Bulgarian wines according to variety and geographic origin. In addition, calibration models between phenolic compounds and Raman spectroscopy were developed using partial least squares (PLS) regression using cross-validation. Good calibration statistics were obtained for total phenolic compounds (by the Folin–Ciocalteu method) and total phenolic compounds and phenolic acids (spectrophotometrically at 280 nm) where the coefficient of determination (R2) and the standard error in the cross-validation (SECV) were 0.81 (474.2 mg/dm3 gallic acid), 0.87 (526.6 mg/dm3 catechin equivalents), and 0.81 (44.8 mg/dm3 caffeic equivalents), respectively. This study has demonstrated that Raman spectroscopy can be suitable for measuring phenolic compounds in both red and white wines.

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