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Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain‐cropped land in the United States (US) as local markets developed for this high‐yielding perennial grass (10–30 Mg DM ha−1). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing‐derived vegetation indices to ground truth data from 105 hand‐harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high‐resolution satellite‐sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha−1) and net profits ( $83 to $ 1211.5 ha−1) was observed. All fields were profitable in all site‐years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of$130 Mg−1 varied from 9.0–12.1 Mg ha−1 at 15% moisture content (7.6–10.3 Mg DM ha−1). Breakeven prices ranged from $ 73 to $122.4 Mg−1, matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial‐scale M × g production. We developed, tested, and used a satellite remote sensing method to predict the productivity and profitability of sterile Miscanthus × giganteus. Using high‐resolution imagery and ground measurements from Iowa's first commercial miscanthus fields, we made a robust model that accurately estimated yield over 8 site‐years. We found strong profitability and identified where better management could further boost returns. This is the first study with actual yield, costs, and returns of commercial miscanthus in the US. It demonstrates that satellite‐based tools can guide efficient and sustainable biomass production, supporting both farmers and industry.

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

Significance Despite often decadeslong control efforts, in many regions of the world ambient concentrations of ground-level ozone threaten human and ecosystem health. Furthermore, in many places the effects of continuing land use and climate change are expected to counteract ongoing efforts to reduce ozone concentrations. Combined with the rising cost of more stringent conventional technological ozone controls, this creates a need to explore novel approaches to reducing tropospheric ozone pollution. Reforestation of peri-urban areas, which removes ozone and one of its precursors, may be a cost-effective approach to ozone control and can produce important ancillary benefits. We identify key criteria for maximizing the ozone abatement and cost effectiveness of such reforestation and the substantial potential for its application in the United States. High ambient ozone (O ₃) concentrations are a widespread and persistent problem globally. Although studies have documented the role of forests in removing O ₃ and one of its precursors, nitrogen dioxide (NO ₂), the cost effectiveness of using peri-urban reforestation for O ₃ abatement purposes has not been examined. We develop a methodology that uses available air quality and meteorological data and simplified forest structure growth-mortality and dry deposition models to assess the performance of reforestation for O ₃ precursor abatement. We apply this methodology to identify the cost-effective design for a hypothetical 405-ha, peri-urban reforestation project in the Houston–Galveston–Brazoria O ₃ nonattainment area in Texas. The project would remove an estimated 310 tons of (t) O ₃ and 58 t NO ₂ total over 30 y. Given its location in a nitrogen oxide (NO ₓ)-limited area, and using the range of Houston area O ₃ production efficiencies to convert forest O ₃ removal to its NO ₓ equivalent, this is equivalent to 127–209 t of the regulated NO ₓ. The cost of reforestation per ton of NO ₓ abated compares favorably to that of additional conventional controls if no land costs are incurred, especially if carbon offsets are generated. Purchasing agricultural lands for reforestation removes this cost advantage, but this problem could be overcome through cost-share opportunities that exist due to the public and conservation benefits of reforestation. Our findings suggest that peri-urban reforestation should be considered in O ₃ control efforts in Houston, other US nonattainment areas, and areas with O ₃ pollution problems in other countries, wherever O ₃ formation is predominantly NO ₓ limited.

To summarize and meta-analyze uveitis characteristics and multiple sclerosis (MS) phenotype of patients with multiple sclerosis-associated uveitis (MSAU) within a systematic review and meta-analysis. A comprehensive literature search was performed on January 25, 2023, utilizing PubMed, Embase, and Virtual Health Library (VHL) databases. We included studies involving patients with MSAU, such as case series with over 10 patients, cross-sectional, case-control, and cohort studies. Quality and risk of bias were assessed using CLARITY tools and validated metrics like the Hoy et al. and Hassan Murad et al. tools. The pooled analysis focused on 1) uveitis characteristics, 2) ocular complications, 3) MS phenotype, and 3) administered treatments for uveitis and MS. Gender-based subgroup analysis was conducted across continents; heterogeneity was measured using the I2 statistic. Statistical analysis was performed using R software version 4.3.1. The study was registered in PROSPERO with CRD42023453495 number. Thirty-six studies were analyzed (24 with a low risk of bias, 8 with some concerns, and 4 with a high risk of bias), including 1,257 patients and 2,034 eyes with MSAU. The pooled analysis showed a mean age of 38.2 ± 12.1 years with a notable female predominance (67%, 95% CI [59%-73%]). MS before uveitis was seen in 59% of the cases (95% CI [48%-69%]), while uveitis was present before MS in 38% (95% CI [30%-48%]). The mean age for the first uveitis episode was 35.7 ± 8.3 years, predominantly affecting both eyes (77%, 95% CI [69%-83%], from 23 studies involving 452 patients). Intermediate uveitis was the most frequent anatomical location (68%, 95% CI [49%-82%], from 22 studies involving 530 patients), often following a recurrent course (63%, 95% CI [38%-83%]). Key complications included vision reduction (42%, 95% CI [19%-70%], from five articles involving 90 eyes), macular compromise (45%, 95% CI [20%-73%], from 4 studies involving 95 eyes), and cataracts (46%, 95% CI [32%-61%], from eight articles involving 230 eyes). Concerning MS phenotype, relapsing-remitting MS (RRMS) was the most common subtype (74%, 95% CI [64%-82%], from eight articles involving 134 patients), followed by secondary progressive MS (24%, 95% CI [18%-33%], from eight articles involving 125 patients). The most frequently occurring central nervous lesions were supratentorial (95%, 95% CI [70%-99%], from two articles involving 17 patients) and spinal cord (39%, 95% CI [16%-68%], from two articles involving 29 patients). The mean Expanded Disability Status Scale (EDSS) score and annual recurrence rates were 2.9 ± 0.6 and 1.07 ± 0.56, respectively. Treatment trends showed the prevalent use of Fingolimod (96%, 95% CI [17%-100%], from two articles involving 196 patients), Mycophenolate (48%, 95% CI [11%-87%], from four articles involving 51 patients), and Interferon-beta (43%, 95% CI [24%-65%], from 11 articles involving 325 patients). MSAU primarily affects young adult females, typically presenting as bilateral intermediate uveitis with vision-related complications. The most common MS phenotype is RRMS, often associated with supratentorial and spinal cord lesions on imaging. These findings give ophthalmologists and neurologists a comprehensive clinical picture of MSAU, facilitating prompt diagnosis.

Recently emerged non-diffracting optical Bessel-Gaussian beams have extended the `optical toolbox' in laser interaction with matter studies, providing many techniques that are not possible using conventional Gaussian beams. The research work performed under this thesis focuses on the design, generation, re-configuration, and exploration of the flexibility of BG beam for a broad range of studies of laser-matter interaction. The numerically modelled BG beam structures are experimentally tested and applied in two substantially different experimental environments. These include the generation and re-imaging of a dynamically varying BG beam, whose morphology is shaped using a Spatial Light Modulator for low-intensity applications and the generation of a static Bessel beam shaped by an axicon to be used with high-intensity femtosecond laser pulses.The first set of experiments explores the construction of a slow-diverging optical funnel based on the generation of a higher-order BG beam using a continuous wave (cw) laser beam and a SLM. The use of hollow-core BG beams have already been demonstrated for pipeline-guiding of sub-micron particles and biological macromolecules in experiments of protein nano-crystallography using x-ray diffracting imaging. The particle dynamical interaction with the beam relies on two fundamental light-induced effects that can be applied on matter, namely, radiation pressure and photophoresis. Here, we generate a SLM-formed BG beam with variable topological charge in combination with different re-imaging systems that enable us to create flexible output beams. We also demonstrate the flexibility in controlling the beam shape, the diameter of the central dark core and the angle of divergence. As a result, the analysis of trajectories of the particles trapped inside the funnel and the modelled intensity distribution of the beam allowed us to evaluate the optical forces exerted on the sub-micron particles and uncover, for the first time in our knowledge, the temperature gradient across the particle surface, which is of paramount importance for laser guiding biological particles in airborne environments.The second set of experiments investigates the construction and re-imaging of zero-order BG beam using axicons for high-intensity laser-induced cylindrical microexplosion in transparent dielectric media. A comparison between numerical modelling and the experimentally constructed BG beam using a commercially available axicons reveals that the origin of the observed undesirable intensity modulations along the beam propagation is due to the imperfections of the axicon shape. That knowledge was applied to manufacture an in-house perfectly shaped axicon and to construct the BG beam which near-ideal intensity distribution highly correlates with the numerical modelled beam. The application of the non-diffracting beam generation setup together with a reimaging system allowed us to produce high aspect-ratio elongated nanovoids inside a pristine sapphire crystal when high-intensity femtosecond laser pulses. Careful consideration of the numerically predicted intensity distribution of the beam inside the crystal and the resulting length and diameter of the nanochannels enable us to demonstrate higher energy concentration in the cylindrical geometry with BG pulses compared to the spherical geometry, common when Gaussian laser pulses were used. The close correlation between numerical and experimental generated beam allowed us, for the first time to our knowledge, to determine the nanochannel formation intensity threshold of 7.2 x 1013 W/cm2 in sapphire.The numerical modelling of the flexible zero- and higher-order BG beams performed in these studies and its application to very different experimental conditions of light-matter interactions is highly advantageous, as they allow tuning of laser parameters in terms of the beam structure and intensity distribution to match the required interaction conditions.

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

The adoption of Remote Sensing (RS) in agriculture have been mainly utilized to inference about biological processes in a scalable manner over space and time. In this context, this work first explores two non-traditional approaches for rapid derivation of plant performance under field conditions. Both approaches focus on plant metrics extraction via high spatial resolution from Unmanned Aerial Systems (UAS). Second, we investigate the spatial-temporal dynamics of corn (Zea mays L.) phenology and yield in the corn belt region utilizing high temporal resolution from satellite. To evaluate the impact of the adoption of RS for deriving plant/crop performance the following objectives were established: i) investigate the implementation of digital aerial photogrammetry to derive plant metrics (plant height and biomass) in corn; ii) implement and test a methodology for detecting and counting corn plants via very high spatial resolution imagery in the context of precision agriculture; iii) derive key phenological metrics of corn via high temporal resolution satellite imagery and identify links between the derived metrics and yield trends over the last 14 years for corn within the corn belt region. For the first objective, main findings indicate that digital aerial photogrammetry can be utilized to derive plant height and assist in plant biomass estimation. Results also suggest that plant biomass predictability significantly increases when integrating the aerial plant height estimate and ground stem diameter. For the second objective, the workflow implemented demonstrates adequate performance to detect and count corn plants in the image. Its robustness highly depends on the spatial resolution of the image, limitations and future research paths are further discussed. Lastly, for the third objective, outcomes indicate that lengthened vegetative and reproductive stages, green-up and senescence rate metrics describe yield increase between 2003 and 2017. Both the spatial and temporal components of the model were significant to describe yield trend. Moreover, when including the temporal component, the model receives lower penalization as an indicator of superior fit on describing yield trend in the region. Overall, the outcomes indicate that in the last 14 years, a significate trend in both space and time on lengthened seasons, faster green-up and senescence rates significantly describe USDA NASS increase on yield in the region. The entire research project investigates opportunities and needs for integrating remote sensing into the agronomic-based inference process.