Using a VNIR Spectral Library to Model Soil Carbon and Total Nitrogen Content

n-situ soil sensor systems based on visible and near infrared spectroscopy is not yet been effectively used due to inadequate studies to utilize legacy spectral libraries under the field conditions. The performance of such systems is significantly affected by spectral discrepancies created by sample intactness and library differences. In this study, four objectives were devised to obtain directives to address these issues. The first objective was to calibrate and evaluate VNIR models statistically and computationally (i.e. computing resource requirement), using four modeling techniques namely: Partial least squares regression (PLS), Artificial neural networks (ANN), Random forests (RF) and Support vector regression (SVR), to predict soil carbon and nitrogen contents for the Rapid Carbon Assessment (RaCA) project. The second objective was to investigate whether VNIR modeling accuracy can be improved by sample stratification. The third objective was to evaluate the usefulness of these calibrated models to predict external soil samples. The final objective was devised to compare four calibration transfer techniques: Direct Standardization (DS), Piecewise Direct Standardization (PDS), External Parameter Orthogonalization (EPO) and spiking, to transfer field sample scans to laboratory scans of dry ground samples. Results showed that non-linear modeling techniques (ANN, RF and SVR) significantly outperform linear modeling technique (PLS) for all soil properties investigated (accuracy of PLS \u3c RF \u3c SVR ≤ ANN). Local models developed using the four auxiliary variables (Region, land use/land cover class, master horizon and textural class) improved the prediction for all properties (especially for PLS models) compared to the global models (in terms of Root Mean Squared Error of Prediction) with master horizon models outperforming other local models. From the calibration transfer study, it was evident that all the calibration transfer techniques (except for DS) can correct for spectral influences caused by sample intactness. EPO and spiking coupled with ANN model calibration showed the highest performance in accounting for the intactness of samples. These findings will be helpful for future efforts in linking legacy spectra to field spectra for successful implementation of the VNIR sensor systems for vertical or horizontal soil characterization. Advisor Yufeng G

Using a VNIR Spectral Library to Model Soil Carbon and Total Nitrogen Content

n-situ soil sensor systems based on visible and near infrared spectroscopy is not yet been effectively used due to inadequate studies to utilize legacy spectral libraries under the field conditions. The performance of such systems is significantly affected by spectral discrepancies created by sample intactness and library differences. In this study, four objectives were devised to obtain directives to address these issues. The first objective was to calibrate and evaluate VNIR models statistically and computationally (i.e. computing resource requirement), using four modeling techniques namely: Partial least squares regression (PLS), Artificial neural networks (ANN), Random forests (RF) and Support vector regression (SVR), to predict soil carbon and nitrogen contents for the Rapid Carbon Assessment (RaCA) project. The second objective was to investigate whether VNIR modeling accuracy can be improved by sample stratification. The third objective was to evaluate the usefulness of these calibrated models to predict external soil samples. The final objective was devised to compare four calibration transfer techniques: Direct Standardization (DS), Piecewise Direct Standardization (PDS), External Parameter Orthogonalization (EPO) and spiking, to transfer field sample scans to laboratory scans of dry ground samples. Results showed that non-linear modeling techniques (ANN, RF and SVR) significantly outperform linear modeling technique (PLS) for all soil properties investigated (accuracy of PLS \u3c RF \u3c SVR ≤ ANN). Local models developed using the four auxiliary variables (Region, land use/land cover class, master horizon and textural class) improved the prediction for all properties (especially for PLS models) compared to the global models (in terms of Root Mean Squared Error of Prediction) with master horizon models outperforming other local models. From the calibration transfer study, it was evident that all the calibration transfer techniques (except for DS) can correct for spectral influences caused by sample intactness. EPO and spiking coupled with ANN model calibration showed the highest performance in accounting for the intactness of samples. These findings will be helpful for future efforts in linking legacy spectra to field spectra for successful implementation of the VNIR sensor systems for vertical or horizontal soil characterization. Advisor Yufeng G

Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization

Moisture is the single most important factor that affects soil reflectance spectra, particularly for field applications. Interest in using soil VNIR spectral libraries, which are commonly based on dry ground soils, to predict soils in the intact field-moist condition (in situ VNIR) is growing. External parameter orthogonalization (EPO) has been proposed as a useful method that links dry ground VNIR models to field moist scans. The goal of this study is to test EPO on a wider set of soil properties and four different modeling techniques, namely, Partial Least Squares Regression (PLS), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM). We selected and scanned 352 archived soil samples fromNebraska, USA, among which 185 samples were used to develop dry groundmodels and the remaining 167 sampleswere rewetted to eight differentmoisture levels for EPO development and testing. Two methods to determine optimum number of EPO components, model-coupled cross validation (Model-Coupled-CV) and Wilk\u27s Λ were also compared. The results showed that EPO minimized the variability of soil spectra induced by moisture. Results suggest a preference for the Wilk\u27s Λ method over Model-Coupled-CV for determining the number of EPO components g, as it produced smoother transformed spectra and more parsimonious models. Among the eight soil properties tested, EPO caused significant improvements for soil Organic Carbon (OC), Inorganic Carbon (IC), and Total Carbon (TC) prediction, marginal improvement for sand and clay, and no improvement for pH, Mehlich-3 Phosphorus, and Cation Exchange Capacity. The failed EPO for the latter three properties is attributable to the poor initial dry-ground models that EPO was built upon. For OC, IC, and TC, EPO coupled effectively with all four modeling methods, with ANN and SVM outperforming the other two slightly. This adds flexibility to the implementation of EPO in predicting field moist soils. As there are increasing demands of spatially-explicit soil data in many disciplines, EPO would be an important essential part for the future in situ VNIR based proximal soil sensing technology

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

USING HYDROGEOPHYSICS & XRF TO PRODUCE A HIGH-RESOLUTION 3-DIMENSIONAL SOIL CADMIUM MAP FOR EVALUATING HYBRID WHEAT TRIALS

Cadmium (Cd) accumulation in wheat decreases germination, growth, grain yield, and in higher concentration leads to adverse effects on human health (Liu et al, 2018). Due to wheat cultivars variation in Cd accumulation, wheat breeders aim to select those at low Cd concentration lines in a field. Hence the need to quantify the concentration of Cd at different parts of a field and visually represent on a high resolution Cd distribution map. Various ways to quantify the concentration of soil Cd exist. However, the cost of equipment required make the process quite expensive and labor intensive. This work studied the feasibility of predicting the concentration of Cd and other soil chemical elements based on readily available environmental covariates collected at the site. These are electrical conductivity in shallow and deep zones (ECaS, ECaD), total gamma counts and elevation. Soil samples were collected from Havelock farm, analyzed in the lab and then results were used to train and test different statistical models to predict the occurrence of chemical elements in the soil. Showed statistical correlation between Geo-covariates and some soil element data (i.e. Zn & Fe) providing proof-of-concept for technique and warranting further investigation • At Havelock Cd was below level of detection • Adding VNIR to Geo-covariates improves prediction accuracy in nonlinear statistical model

Variation in morpho‑physiological and metabolic responses to low nitrogen stress across the sorghum association panel

Background: Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. However, our understanding of changes in different phenotypes under long term low nitrogen stress and their impact on important agronomic traits, such as yield, is still limited. Results: Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress. Several metabolites were associated with yield under high and low nitrogen conditions. Conclusion: Our results highlight that grain yield in sorghum, unlike many morpho-physiological traits, exhibits substantial variability of genotype specific responses to long term low severity nitrogen deficit stress. Metabolic response to long term nitrogen stress shown higher proportion of variability explained by genotype specific responses than did morpho-pysiological traits and several metabolites were correlated with yield. This suggest, that it might be possible to build predictive models using metabolite abundance to estimate which sorghum genotypes will exhibit greater or lesser decreases in yield in response to nitrogen deficit, however further research needs to be done to evaluate such model

Design, Development, and Field Testing a VisNIR Integrated Multi-sensing Soil Penetrometer

The research community in soil science and agriculture lacks a cost-effective and rapid technology for in situ, high resolution vertical soil sensing. Visible and near infra-red (VisNIR) technology has the potential to be used for such sensor development due to its ability to derive multiple soil properties rapidly using a single spectrum. Such efforts must, however, overcome a few challenges: (i) a dry ground soil spectral library that can be used to predict the target soil properties accurately, (ii) a robust design which can acquire high quality VisNIR spectra of soil, (iii) an effective method that can link field intact soil spectra to the dry ground spectra in the library. The overall goal of the work presented in this dissertation was to design, develop, and test a VisNIR integrated multi-sensing penetrometer to estimate soil properties in vertical profile. To achieve this goal, three specific objectives were developed. The first was to investigate and compare the usefulness of five approaches: External Parameter Orthogonalization (EPO), Direct Standardization (DS), Global Moisture Modeling (GMM), Slope Bias Correction (SB) and Selective Wavelength Modeling (SWM), in enabling VisNIR dry ground models to be applied directly to moist soil spectra to predict soil organic carbon and inorganic carbon. The second was to design new VisNIR probes and test them in terms of spectral quality and predictive power using an external spectral library under laboratory conditions. Third was to develop the fully integrated, multi-sensing penetrometer system for high resolution vertical soil sensing and field test the penetrometer to evaluate its performance. The results showed that EPO, DS and GMM account satisfactorily for the effect of moisture in soil spectra. The VisNIR probe developed showed high spectral quality, however with a systematic difference compared to standard MugLite® spectra which was successfully rectified by DS or spiking. The final designed fully integrated, multi-sensing penetrometer system, could estimate soil properties: total carbon, total nitrogen and bulk density, in vertical soil profile with EPO to correct for field intactness. This can lead to a rapid, robust and cost-effective penetrometer system for in situ high resolution vertical soil sensing in the future

Design, Development, and Field Testing a VisNIR Integrated Multi-sensing Soil Penetrometer

The research community in soil science and agriculture lacks a cost-effective and rapid technology for in situ, high resolution vertical soil sensing. Visible and near infra-red (VisNIR) technology has the potential to be used for such sensor development due to its ability to derive multiple soil properties rapidly using a single spectrum. Such efforts must, however, overcome a few challenges: (i) a dry ground soil spectral library that can be used to predict the target soil properties accurately, (ii) a robust design which can acquire high quality VisNIR spectra of soil, (iii) an effective method that can link field intact soil spectra to the dry ground spectra in the library. The overall goal of the work presented in this dissertation was to design, develop, and test a VisNIR integrated multi-sensing penetrometer to estimate soil properties in vertical profile. To achieve this goal, three specific objectives were developed. The first was to investigate and compare the usefulness of five approaches: External Parameter Orthogonalization (EPO), Direct Standardization (DS), Global Moisture Modeling (GMM), Slope Bias Correction (SB) and Selective Wavelength Modeling (SWM), in enabling VisNIR dry ground models to be applied directly to moist soil spectra to predict soil organic carbon and inorganic carbon. The second was to design new VisNIR probes and test them in terms of spectral quality and predictive power using an external spectral library under laboratory conditions. Third was to develop the fully integrated, multi-sensing penetrometer system for high resolution vertical soil sensing and field test the penetrometer to evaluate its performance. The results showed that EPO, DS and GMM account satisfactorily for the effect of moisture in soil spectra. The VisNIR probe developed showed high spectral quality, however with a systematic difference compared to standard MugLite® spectra which was successfully rectified by DS or spiking. The final designed fully integrated, multi-sensing penetrometer system, could estimate soil properties: total carbon, total nitrogen and bulk density, in vertical soil profile with EPO to correct for field intactness. This can lead to a rapid, robust and cost-effective penetrometer system for in situ high resolution vertical soil sensing in the future

Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery

Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R2 = 0.68), Maturity (RMSE = 3.70, R2 = 0.76) and Seed Size (RMSE = 1.63, R2 = 0.53) were identified as potential soybean traits that might be early predictable