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(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel−1) and 50 m (35 mm pixel−1) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R2 values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps. [ABSTRACT FROM AUTHOR]

Proximal sensing technologies can densely quantify soil organic matter (OM) variability using visible and near infrared (VNIR) reflectance spectroscopy. However, issues with global calibrations and varying water content can reduce accuracy. Therefore, research was conducted to determine OM prediction accuracy across soil volumetric water content (VWC) with (a) a planter‐mounted optical sensor and (b) multiple combinations of reflectance bands within the VNIR spectrum. Ninety soils collected across Missouri and Illinois were used in the study. Data were collected at three VWC with the Precision Planting SmartFirmer and a benchtop spectrometer. SmartFirmer OM was derived through an internal algorithm, while spectral pre‐processing and machine learning were used for OM prediction in all approaches that used reflectance from the benchtop spectrometer. Results found that SmartFirmer OM predictions were affected by soil VWC, with predicted OM decreasing with increasing VWC. Findings from three modeling approaches using the benchtop spectrometer showed that a continuous spectrum (i.e., 400–1,500 nm) improved performance (RMSE = 5.25 g kg–1) over the discrete waveband approach (RMSE = 9.23 g kg–1). Furthermore, including the entire VNIR region (400–2,500 nm) resulted in the best predictive capability (RMSE = 1.42 g kg–1). However, because a full‐spectrum approach increases cost, using reflectance from 400 to 1,500 nm along with spectral pre‐processing and machine learning may be an acceptable method for estimating OM. These findings contribute to the improvement of commercially available proximal sensors that may be used to monitor soil carbon (C) stocks, assess soil health, or for other precision agriculture applications. Core Ideas: Commercial sensing technology was able to detect general differences in soil organic matter levels.Regional calibration provided little improvement over the commercial sensor when using the same targeted wavebands.The partial spectral range (400–1,500 nm) resulted in the best combination of accuracy and simplicity. [ABSTRACT FROM AUTHOR]

Proximal soil sensing is an attractive approach for quantifying soil properties, but many currently available sensors do not respond to a single soil property. For example, soil strength and apparent electrical conductivity (EC a ) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential for estimating soil texture, BD, and WC using combinations of sensor-based soil strength and EC a data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 0.5 m on a 0.1-m interval, cone index measurements at the same depths, EC a measured by a Veris 3100, and depth-dependent, laboratory-determined soil properties. An EC a model inversion approach was used to generate layer EC values corresponding to the depth increments of the other variables. Fits of models using EC to estimate WC were variable (R 2 = 0.31–0.79). Best fitting BD estimation models (R 2 = 0.11–0.55) generally included EC, but soil strength was included in fewer than half of the models. BD model fits were improved considerably by adding lab-measured WC to the model (R 2 = 0.30–0.86), suggesting the need for a WC sensor. Soil clay texture fraction models based on EC and WC fit well (R 2 = 0.80–0.93). This study showed the potential of combining data from multiple mobile proximal sensors to estimate important soil physical properties. [ABSTRACT FROM AUTHOR]

Capturing the variability in soil-landscape properties is a challenge for grain producers attempting to integrate spatial information into the decision process of precision agriculture (PA). Digital soil maps (DSMs) use traditional soil survey information and can be the basis for PA subfield delineation (e.g., management zones). However, public soil survey maps provide only general descriptions of soil-landscape features. Therefore, improved DSMs are needed that use high-resolution data that more precisely model soil-landscape characteristics. Additionally, reliable methods are needed to validate DSM products for PA. The objective of this study was to validate with corn (Zea mays L.) yield data the performance of a new DSM product, termed Environmental Response Unit (ERU), compared with the USDA Soil Survey Geographic (SSURGO) soil map. The ERU was developed by integrating SSURGO information with high-resolution elevation data. For validation, corn yield maps were collected and corrected for common data collection errors from 409 fields across Indiana, Iowa, Minnesota, and Nebraska in 2010 to 2012. Reductions in the area-weighted variance (Rv) of corn yield for ERU and SSURGO were calculated relative to the whole-field variance. The average Rv across all site-years for SSURGO and ERU was 16 and 25%, respectively, which equated to a 57% higher median yield variance reduction with ERU over SSURGO. This variance reduction technique showed the potential of ERU as an improved model better representing soil-landscape properties that impact corn yield. This research also has application potential for determining the success of a DSM for identifying management zones in PA. [ABSTRACT FROM AUTHOR]

Abstract: Soil compaction that is induced by tillage and traction is an ongoing concern in crop production, and also has environmental consequences. Although cone penetrometers provide standardized compaction measurements, the pointwise data collected makes it difficult to obtain enough data to represent within-field variability. Moreover, penetrometer data exhibit considerable variability even at a single location, requiring several measurements to obtain representative readings. For more efficient data collection, on-the-go compaction sensors that obtain data at multiple depths are being developed by several research groups. The objective of this research was to evaluate and compare the field performance of two on-the-go compaction sensors. Tests were conducted at two central Missouri field sites where soil types ranged from sandy loam to clay. The soil strength profile sensor (SSPS) measured compaction to a 50-cm depth on 10-cm intervals. The soil compaction profile sensor (SCPS) also used five sensing elements and obtained data to 40.6cm on a 7.6-cm interval. Cone penetrometer measurements of compaction were obtained at intervals along each transect for comparison. Data were compared between the two on-the-go sensors and were also related to penetrometer and soil property data. The repeatability of SCPS data was somewhat better than that of SSPS data. Data from the two sensors were linearly related, with similar regression equations for each individual site and for both sites combined. The agreement between SCPS and SSPS data (r 2 =0.56 over all sites and depths) was much better than between sensor and penetrometer data (r 2 =0.19–0.20). Maps of SCPS and SSPS data for a 13.5-ha field site showed very similar patterns. Maps of penetrometer data were also similar to those of on-the-go sensor data, but showed fewer spatial details. Variation in soil strength appeared to be primarily related to variations in soil physical properties (e.g., texture, water content). Due to the similarity between SCPS and SSPS data, we conclude that measurements obtained with the two on-the-go soil sensors were affected similarly by soil strength variations within the study sites. Side-by-side comparison of the on-the-go sensors provided a convenient approach to validate sensor performance. The study also provided information to improve on-the-go sensor design and to relate sensor data to other measures of soil compaction. [Copyright &y& Elsevier]

In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of soil moisture, other environmental factors, and artefacts of the in-field spectral data collection process often hamper the utility of in situ DRS data. Various processing and modeling techniques have been developed to overcome these challenges, including external parameter orthogonalization (EPO) transformation of the spectra. In addition, Bayesian modeling approaches may improve prediction over traditional partial least squares (PLS) regression. The objectives of this study were to predict soil organic carbon (SOC), total nitrogen (TN), and texture fractions using a large, regional dataset of in situ profile DRS spectra and compare the performance of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models. In this study, soil cores and in situ profile DRS spectrometer scans were obtained to ~1 m depth from 22 fields across Missouri and Indiana, USA. In the laboratory, soil cores were split by horizon, air-dried, and sieved (<2 mm) for a total of 708 samples. Soil properties were measured and DRS spectra were collected on these air-dried soil samples. The data were randomly split into training (n = 308), testing (n = 200), and EPO calibration (n = 200) sets, and soil textural class was used as the categorical covariate in the Bayesian models. Model performance was evaluated using the root mean square error of prediction (RMSEP). For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing RMSEP by 66% and 53% for SOC and TN, respectively, and by 76%, 91%, and 87% for clay, silt, and sand, respectively. The addition of the Bayesian Lasso further reduced RMSEP by 4–11% across soil properties, and the categorical covariate reduced RMSEP by another 2–9%. Overall, this study illustrates the strength of the combination of EPO spectral transformation paired with Bayesian modeling techniques to overcome environmental factors and in-field data collection artefacts when using in situ DRS data, and highlights the potential for in-field DRS spectroscopy as a tool for rapid, high-resolution prediction of soil properties. [ABSTRACT FROM AUTHOR]

• An image processing workflow to geo-reference single UAV images. • Field mapping of crop early emergence using individual UAV images. • Improved position accuracy of 0.17 and 0.57 m for cotton and corn UAV images. • Potential real-time processing UAV images for on-site decision making. Unmanned aerial vehicle (UAV) based remote sensing has been extensively used in precision agriculture applications, such as vegetation growth and health monitoring, yield estimation, and irrigation management. Conventional procedures for UAV data collection and processing require collecting highly overlapped images, stitching images to generate an orthomosaic, and using ground control points (GCPs) in the field or UAV onboard real-time-kinematic (RTK) global navigation satellite system (GNSS) data to improve position accuracy. For improving efficiency, a previous study developed a framework to process individual UAV images for mapping cotton emergence. The current study aimed to build a near-real time image processing pipeline to further improve the positioning accuracy of single UAV images. The improved image processing pipeline comprised feature detection and matching, false matches removal, geometric transformation matrix calculation, crop row alignment, image position assignment, and mapping. The developed pipeline was tested for mapping in both cotton and corn fields. Results showed that the position accuracies for measuring the distance between GCPs were 0.32 ± 0.21 m and 0.57 ± 0.28 m in a cotton and a corn field, respectively, when compared to ground truth data collected with an RTK-GNSS. The developed pipeline did not require GCPs in the field or image post-processing steps, such as image mosaicking and feature extraction, which allowed processing in near-real time and may possibly be implemented in real-time using an onboard edge computing system. The pipeline was used to map emergence parameters for cotton and corn fields, including stand count, canopy area, mean days to imaging after emergence, and plant spacing standard deviation. These maps demonstrated the success of the developed methods in providing a low-cost near real-time tool (8.6 and 3.6 s/image for the cotton and corn fields, respectively) for mapping emergence parameters at field-scale for use in both research and agricultural production. [ABSTRACT FROM AUTHOR]