Highlights This study presents a seven-year operational review of a large-scale cable-suspended phenotyping system. Standardized operation and data protocols enable the delivery of high-resolution, high-frequency phenotypic datasets. The platform supports a wide range of phenotyping applications, from morphological to physiological trait analysis. The system plays a critical role in developing advanced sensing technology for phenotyping and precision agriculture. ABSTRACT. High-throughput plant phenotyping (HTPP) significantly improves plant phenotyping efficiency by integrating advanced sensing technologies, data processing, and modeling techniques. Over the last two decades, field-based HTPP systems have evolved from handheld sensors to sophisticated robotic platforms. Large-scale, ground-based phenotyping facilities have made significant contributions to advancing technology through their high sensor payloads, proximal measurement capabilities, and unmatched spatial and temporal resolution. This review paper presents a detailed and quantitative analysis of the operational performance of the cable-suspended NU-Spidercam phenotyping facility at the University of Nebraska–Lincoln from 2017 to 2023, focusing on daily operations, data management strategies, and system maintenance. Additionally, the paper systematically summarizes representative studies performed at the facility across morphological, spectral, and physiological phenotyping domains. Finally, the discussion highlights future directions, emphasizing the NU-Spidercam’s role in validating mobile phenotyping platforms, enabling precision agriculture research, supporting fundamental remote sensing studies, and facilitating the transfer of advanced sensing techniques to affordable, mobile platforms such as drones. Keywords: Artificial intelligence, Computer vision, Deep learning, Field plant phenotyping, Large-scale facility, Machine learning, Operational review, Physiological phenotyping.

Context Platforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. Objective In this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. Methods The new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. Results The result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R2 values ranging from 0.35 to 0.93 (average R2 = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. Conclusion High spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. Significances Our work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation.

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed radiation (ARAD), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated ARAD, accounting for changes in biomass composition. The RUE and SLN exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages and underestimated the maximum RUE observed during pod setting. Comparison between observed and simulated RUE using a process-based crop simulation model revealed similar discrepancies. The decoupling between RUE and SLN during early crop stages suggests that leaf N is above that needed to maximize crop growth but may play a role in storing N that can be used in later reproductive stages to meet the large seed N demand associated with high-yielding crops.

The Bidirectional Reflectance Distribution Function (BRDF) quantifies the distribution of the spectral reflectance of a target surface at various viewing and illumination angles. In-field measurement of the BRDF of vegetation canopies improves the characterization of off-nadir measurements and informs radiative transfer models of canopy reflectance, where the Lambertian assumption does not hold. However, current field goniometers are unable to measure BRDF efficiently, especially for tall vegetation across the growing season because of the limitations of clearance, field accessibility, and flexibility of the sensor field of view. In this study, we explored the potential of using a large-scale cable-suspended field phenotyping system to quantify BRDF of the canopy reflectance at selected bands and Vegetation Indices (VIs) of maize and soybean canopies. The system performance benefited from the following conditions: no crop damage, full-season field accessibility of tall canopies, automatic measurement, and accurate positioning. Correlation analysis shows that a strong correlation exists among the reflectance, VIs, and the Green Pixel Fraction. The hemispheric distributions of the spectral reflectance at selected bands and VIs were quantified at multiple dates. Hot spots were observed in the backscatter direction at visible and near-infrared (NIR) bands at the largest sensor zenith angle around the Solar Principal Plane (SPP). In contrast, cold spots of the Normalized Difference Vegetation Index (NDVI) and its related VIs were observed in the backscatter direction around solar zenith angles. The row effect was found for the maize canopy at the NIR band and the Near-infrared reflectance of vegetation (NIRv). NDVI had the lowest anisotropy index values among investigated bands and VIs. This system could be further leveraged to generate rapid and detailed BRDF data at a high spatiotemporal resolution using multiple sensors.

33 Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation 34 in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated 35 this hypothesis using data on leaf area, absorbed photosynthetically active radiation (APAR), 36 aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire 37 crop season from seven field experiments conducted in a stress-free environment. Each 38 experiment included a full N treatment that received ample N fertilizer and a zero N treatment 39 that relied on N fixation and soil N mineralization. We estimated RUE based on changes in 40 ADM between sampling times and associated APAR, accounting for changes in biomass 41 composition. The SLN and RUE exhibited different seasonal patterns: a bell-shaped pattern with 42 a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline 43 during late seed filling, respectively. The level of N supply influenced SLN more than RUE via 44 changes in leaf N concentration, with small changes in specific leaf weight. Changes in SLN 45 explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier 46 stages. A simple approach based on phenological stages may give more realistic estimates of 47 RUE before seed filling, improving crop growth and yield prediction via crop models and remote 48 sensing. 49

Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed photosynthetically active radiation (APAR), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated APAR, accounting for changes in biomass composition. The SLN and RUE exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. The level of N supply influenced SLN more than RUE via changes in leaf N concentration, with small changes in specific leaf weight. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages. A simple approach based on phenological stages may give more realistic estimates of RUE before seed filling, improving crop growth and yield prediction via crop models and remote sensing. Highlight Changes in radiation-use efficiency during soybean vegetative and early reproductive stages are not related to specific leaf nitrogen.

Tropospheric ozone (O3) is the third most important anthropogenic greenhouse gas. O3 is detrimental to plant productivity, and it has a significant impact on crop yield. Currently, the Joint UK Land Environment Simulator (JULES) land surface model includes a representation of global crops (JULES-crop) but does not have crop-specific O3 damage parameters and applies default C3 grass O3 parameters for soybean that underestimate O3 damage. Physiological parameters for O3 damage in soybean in JULEScrop were calibrated against leaf gas-exchange measurements from the Soybean Free Air Concentration Enrichment (SoyFACE) with O3 experiment in Illinois, USA. Other plant parameters were calibrated using an extensive array of soybean observations such as crop height and leaf carbon and meteorological data from FLUXNET sites near Mead, Nebraska, USA. The yield, aboveground carbon, and leaf area index (LAI) of soybean from the SoyFACE experiment were used to evaluate the newly calibrated parameters. The result shows good performance for yield, with the modelled yield being within the spread of the SoyFACE observations. Although JULES-crop is able to reproduce observed LAI seasonality, its magnitude is underestimated. The newly calibrated version of JULES will be applied regionally and globally in future JULES simulations. This study helps to build a state-of-the-art impact assessment model and contribute to a more complete understanding of the impacts of climate change on food production.

Field-based high throughput plant phenotyping has recently gained increased interest in the efforts to bridge the genotyping and phenotyping gap and accelerate plant breeding for crop improvement. In this paper, we introduce a large-scale, integrated robotic cable-driven sensing system developed at University of Nebraska for field phenotyping research. It is constructed to collect data from a 0.4 ha field. The system has a sensor payload of 30 kg and offers the flexibility to integrate user defined sensing modules. Currently it integrates a four-band multispectral camera, a thermal infrared camera, a 3D scanning LiDAR, and a portable visible near-infrared spectrometer for plant measurements. Software is designed and developed for instrument control, task planning, and motion control, which enables precise and flexible phenotypic data collection at the plot level. The system also includes a variable-rate subsurface drip irrigation to control water application rates, and an automated weather station to log environmental variables. The system has been in operation for the 2017 and 2018 growing seasons. We demonstrate that the system is reliable and robust, and that fully automated data collection is feasible. Sensor and image data are of high quality in comparison to the ground truth measurements, and capture various aspects of plant traits such as height, ground cover and spectral reflectance. We present two novel datasets enabled by the system, including a plot-level thermal infrared image time-series during a day, and the signal of solar induced chlorophyll fluorescence from canopy reflectance. It is anticipated that the availability of this automated phenotyping system will benefit research in field phenotyping, remote sensing, agronomy, and related disciplines.

The JULES-crop model (Osborne et al., 2015) is a parametrisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of croplands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2 and US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties.

This study utilized a leaf color chart (LCC) to characterize the variation in leaf chlorophyll and estimate canopy chlorophyll in maize (Zea mays). The LCC consisted of four levels of greenness and was used to sort maize leaves in 2011 for three fields near Mead, Nebraska, USA. Leaf chlorophyll content for each color chart class was determined using two leaf-level sensors. The variation within each LCC class was reasonable (CV < 56%). The darkest color class predominated and indicated adequate fertilization rates using a Minolta SPAD-502 meter. Canopy chlorophyll content was estimated using destructively measured leaf area index (LAI) and the LCC. This approach was verified with a method utilizing canopy reflectance collected by both satellite imagery and a four-band radiometer. The error between the two methods was reasonable (RMSE = 0.55–0.88 g m−2; CV = 25.6–50.4%), indicating that both leaf and canopy chlorophyll can be estimated cheaply without a wet lab or field-based sensors.