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Increasing interest in deploying multiple types of sensing instruments for agricultural plot‐level observations has created a need for simple, high‐clearance vehicles that can be easily maneuvered through crops while minimizing damage due to wheel traffic. We describe a simple cart built from a 2‐m‐wide by 1.2‐m‐long steel frame that was welded onto two bicycle frames at a height providing 1 m of vertical clearance. Instruments such as radiometers and infrared thermometers are attached to the frame via arms that are secured with U‐bolts. A large, horizontal surface allows mounting data loggers, batteries, or computers. The cart is easily maneuvered by one person on level ground or by two persons on terrain with furrows, berms, or other obstacles. Design sketches and lists of materials are provided in an electronic supplement. The basic design is readily modifiable for different interrow spacings and sensor positions.

Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.

Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.

Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.

The development of management plans which lead to water efficient landscapes is a growing need in the turfgrass community. While deficit irrigation as a scheduling method can improve water conservation, more information is desired on how to best leverage other management practices, such as mowing height when deficit irrigation is imposed. The objectives of this study were to characterize actual evapotranspiration (ETa), turfgrass visual quality, clipping production, and root development of ‘TifTuf’ bermudagrass (Cynodon dactylon × C. transvaalensis Burt Davy) when irrigated at full (1.0 × ETa) and deficit levels (0.65 and 0.30 × ETa), and cut at four separate mowing heights (2.5, 5.0, 7.5, and 10.0 cm) over two 8-week experimental runs. An elevated ETa was observed at the 7.5 cm and 10.0 cm mowing heights compared to the 2.5 cm mowing height in both runs, and the 5.0 cm mowing height in one run. The visual quality decreased throughout both study periods and mostly for the deficit irrigation treatments, with visual quality falling below minimum acceptable levels at the lowest irrigation level (0.30 × ETa) 5 weeks into run A, and 8 weeks into run B. Despite an elevated ETa and a higher root dry weight at higher mowing heights (7.5 and 10.0 cm), clipping production and visual quality was generally higher at lower mowing heights (2.5 and 5.0 cm) for both full and deficit irrigation levels. These results demonstrate that mowing height can significantly influence bermudagrass water use, as well as responses to deficit irrigation. When maintaining ‘TifTuf’ bermudagrass at heights above 2.5 cm, the results from this study indicate a lower water use and improved response to deficit irrigation at mowing heights ≤ 5 cm.

Water scarcity poses a substantial challenge for turfgrass irrigation in the drought- and heat-stressed Desert Southwest region of the United States. Bermudagrass (Cynodon spp.), renowned for its exceptional drought resistance, is the predominant warm-season turfgrass in the region. Selecting and using drought-resistant bermudagrass cultivars remains a primary strategy for sustaining the turfgrass industry in the region. This study evaluated 48 hybrid bermudagrasses (Cynodon dactylon × C. transvaalensis Burtt-Davy), including two commercial cultivars (‘TifTuf’ and ‘Tifway’, as controls), under 80% × ETo (0.8ET), 60% × ETo (0.6ET) and 40% × ETo (0.4ET) reference evapotranspiration (ETo) replacement irrigation systems at Maricopa, AZ. The experiment was laid out in a split-plot design with two replications, where the 3 irrigation treatments were assigned to main plots and 48 genotypes were in sub-plots. Analysis of data from two years (2022 and 2023) revealed significant differences among bermudagrass hybrids, irrigation treatments, and their interaction effects. The hybrids exhibited substantial variation for spring green-up, density, turf color, and quality. With the largest deficit irrigation treatment 40% × ETo (0.4ET), OSU2104, OSU2106, and OSU2105 showed greater mean greenness and aesthetic quality scores than recorded for ‘TifTuf’ (6.5), a popular drought-tolerant cultivar. The results highlight the prevalence of genetic variation in germplasm with potential for development of improved varieties for drought tolerance.

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland ( L.) recombinant inbred line (RIL) population of 95 lines and a Pima ( L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination ([Formula: see text] = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

Background: Advancements in field spectrometry have the potential to increase understanding of crop growth and development in response to hot and dry environments. However, as with any instrument used for scientific advancement, it is important to continue developing and optimizing data collection protocols to promote efficiency, safety, and data quality. The goal of this study was to develop a novel data collection method, involving a proximal sensing cart with onboard cooling equipment, to improve deployments of a field spectroradiometer in a hot and dry environment. Advantages and disadvantages of the new method were compared with the traditional backpack approach and other approaches reported in literature.Results: The novel method prevented the spectroradiometer from overheating and nearly eliminated the need to halt data collection for battery changes. It also enabled data collection from a significantly larger field area and from more field plots as compared to the traditional backpack method. Use of a custom cooling box to stabilize operating temperatures for the field spectroradiometer also improved stability of white panel data both within and among collections despite outside air temperatures in excess of 30°C.Conclusions: As compared to traditional data collection approaches for measuring spectral reflectance of field crops in a hot and dry environment, use of a proximal sensing cart with a customized equipment cooling box improved spectroradiometer performance, increased practicality of equipment transport, and reduced operator safety concerns.

Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.