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Most current marine aquaculture operations are located in coastal estuarine areas within one mile of the shoreline, and water quality in these production areas can quickly become unfavorable due to hydrodynamic processes and excessive runoff. The deployment of autonomous, robotic systems can improve the speed and spatiotemporal resolution of water sampling and sensing in mariculture production areas to assess water quality in the context of food safety. Specifically, teams of both aerial and surface vehicles can be deployed simultaneously to capitalize on the benefits of each system; however, a method to optimally design a feasible sampling tour for each robot is needed to maximize sample capacity and ensure efficient water sampling missions. This research brief presents the problem formulation and a solution method to determine optimal tours for a team of aquatic surface and aerial vehicles while considering different vehicle sampling capacities and endurance constraints. This method was implemented to design sampling missions of 15, 20, and 30 samples in both a 0.25 km2 and 3.9 km2 site, using sampling capacity and endurance constraints corresponding to real-world robots used for water sampling in mariculture environments. Results indicate that this optimization problem can be solved in nearreal time in the field and yields feasible sampling tours for surface and aerial vehicles under different constraints. This work is a practical step towards developing teams of collaborative robots to persistently monitor adverse mariculture growing conditions so producers can implement data-driven, timely management strategies. [ABSTRACT FROM AUTHOR]

Background: Soil organic C and N data from privately managed pastures in the southeastern United States are relatively scant. Methods: A paired‐farm approach was deployed to determine how a variety of soil health parameters related to nutrient and water cycling might be altered under grazed, botanically diverse perennial pastures compared with annual monoculture croplands in three Major Land Resource Areas of the southeastern United States. Results: Soil stability index averaged 0.64 and 0.91 mm mm−1 under cropland and grazed pasture, respectively, suggesting that pastures had a more stable soil surface that was resistant to erosion and allowed rapid water infiltration. Surface‐soil organic C and N fractions (i.e., total, particulate, and mineralizable fractions at 0–10 cm depth) were greater under pasture than under cropland. Across locations, root‐zone enrichments (0–30 cm depth) of organic C and N fractions were greater under pasture than under cropland. Within locations, root‐zone enrichment of total soil N was greater (p < 0.05) under pasture than under cropland in the Blue Ridge (2.87 vs. 1.10 Mg N ha−1, respectively) and the Piedmont (2.80 vs. 2.10 Mg N ha−1), but not in the Blackland Prairie (2.40 vs. 2.12 Mg N ha−1). Conclusions: This study provides evidence that rotationally grazed, perennial grasslands can store more soil organic C and N and improve soil surface stability conditions compared with neighboring croplands producing commodity feed grains for feedlot finishing. [ABSTRACT FROM AUTHOR]

As much as users and advertisers have flocked to it, TikTok has been a success. To this point, research on TikTok has been concerned with the nature of TikTok texts and the ways that laterally organized networks on TikTok have intermedia interface points. Instead of focusing on the text or the authors, this article argues that Raymond Williams concept of flow is critical for an initial characterization of a social media platform, proposing and implementing a Markov process model for critical media studies of the experience of TikTok flow. The result of this analysis challenges many commonly held ideas about what TikTok "is" and offers evidence that TikTok is television, not social media. We argue that this shift of perspective is important for advancing understanding of this platform. [ABSTRACT FROM AUTHOR]

This article presents a computer vision-based approach for monitoring water flow at outlet points of a tile drain system. The approach relies only on video capture of events at outlet points, thus a camera can be installed remotely and without contact with water. The algorithm detects, identifies, and tracks flows by motion, shape, and color features and measures flow rate based on a proposed model and two provided dimensions. The software was tested in a laboratory environment with three different target flow rate conditions: 0.312, 0.946, and 1.58 L/s (5, 15, and 25 gal/min). Flow rates reported by the computer vision approach are within 12% of the ground-truth flow rate baseline. The results of this work show that computer vision can be used as a reliable method for monitoring outlet flows from free-standing outlet structures under laboratory conditions. This work opens the possibility of applying computer vision techniques in tile drain monitoring from outlet points with mobile video recording devices in the field. [ABSTRACT FROM AUTHOR]

Floral hemp cultivated for the extraction of cannabinoids is a new crop in the United States, and agronomic recommendations are scarce. The objective of this study was to understand the effects of plant spacing and transplant date on floral hemp growth and biomass production. Field trials were conducted in North Carolina in 2020 and 2021 with the floral hemp cultivar BaOx. Transplant date treatments occurred every two weeks from 11 May to 7 July (±1 d). Plant spacing treatments were 0.91, 1.22, 1.52, and 1.83 m between plants. Weekly height and width data were collected throughout the vegetative period, and dry biomass was measured at harvest. Plant width was affected by transplant date and spacing. Plant height was affected by transplant date. Earlier transplant dates resulted in taller, wider plants, while larger plant spacing resulted in wider plants. Individual plant biomass increased with earlier transplant dates and larger plant spacing. On a per-hectare basis, biomass increased with earlier transplant dates and smaller transplant spacing. An economic analysis found that returns were highest with 1.22 m spacing and decreased linearly by a rate of −163.098 USD ha−1 d−1. These findings highlight the importance of earlier transplant timing to maximize harvestable biomass. [ABSTRACT FROM AUTHOR]

As an emerging cash crop, industrial hemp (Cannabis sativa L.) grown for cannabidiol (CBD) has spurred a surge of interest in the United States. Cultivar selection and harvest timing are important to produce CBD hemp profitably and avoid economic loss resulting from the tetrahydrocannabinol (THC) concentration in the crop exceeding regulatory limits. Hence there is a need for differentiating CBD hemp cultivars and growth stages to aid in cultivar and genotype selection and optimization of harvest timing. Current methods that rely on visual assessment of plant phenotypes and chemical procedures are limited because of its subjective and destructive nature. In this study, hyperspectral imaging was proposed as a novel, objective, and non-destructive method for differentiating hemp cultivars, growth stages as well as plant organs (leaves and flowers). Five cultivars of CBD hemp were grown greenhouse conditions and leaves and flowers were sampled at five growth stages 2–10 weeks in 2-week intervals after flower initiation and scanned by a benchtop hyperspectral imaging system in the spectral range of 400–1000 nm. The acquired images were subjected to image processing procedures to extract the spectra of hemp samples. The spectral profiles and scatter plots of principal component analysis of the spectral data revealed a certain degree of separation between hemp cultivars, growth stages, and plant organs. Machine learning based on regularized linear discriminant analysis achieved the accuracy of up to 99.6% in differentiating the five hemp cultivars. Plant organ and growth stage need to be factored into model development for hemp cultivar classification. The classification models achieved 100% accuracy in differentiating the five growth stages and two plant organs. This study demonstrates the effectiveness of hyperspectral imaging for differentiating cultivars, growth stages and plant organs of CBD hemp, which is a potentially useful tool for growers and breeders of CBD hemp. [ABSTRACT FROM AUTHOR]

Web applications, also known as web apps, are increasingly common in the research communication portfolios of those working in the life sciences (e.g., [[1]]) and physical sciences (e.g., [[2]-[4]]). Graph: Fig 1 Major components of a web application (web app).In this line drawing, the (a) user is shown interacting with the (b) front end of the web app. Whenever possible, build your web app using widely supported technologies and include a test suite (see Rule 2) to ensure that the web app code will function properly as an ensemble after you have made changes to the source code and web app dependencies (i.e., the software and code versions that your web app depends on to run). To ensure long-term maintenance and utility of your web app, determine who will be designated as the web app maintainer(s) [[58], [70]] and how long-term web app ownership and maintenance is defined [[71]]. Last but not least, web app developers can use web accessibility evaluation tools to scan their web app for accessibility issues and implement solutions to these issues via updates to front end design and back end scripts. [Extracted from the article]

The most important climatic variable influencing growth and survival of loblolly pine is the yearly average minimum winter temperature (MWT) at the seed source origin, and it is used to guide the transfer of improved seed lots throughout the species' distribution. This study presents a novel approach for the assessment of freeze-induced damage and prediction of MWT at seed source origin of loblolly pine seedlings using hyperspectral imaging. A population comprising 98 seed lots representing a wide range of MWT at seed source origin was subjected to an artificial freeze event. The visual assessment of freeze damage and MWT were evaluated at the family level and modeled with hyperspectral image data combined with chemometric techniques. Hyperspectral scanning of the seedlings was conducted prior to the freeze event and on four occasions periodically after the freeze. A significant relationship (R 2 = 0.33; p <.001) between freeze damage and MWT was observed. Prediction accuracies of freeze damage and MWT based on hyperspectral data varied among seedling portions (full-length, top, middle, and bottom portion of aboveground material) and scanning dates. Models based on the top portion were the most predictive of both freeze damage and MWT. The highest prediction accuracy of MWT [RPD (ratio of prediction to deviation) = 2.12, R 2 = 0.78] was achieved using hyperspectral data obtained prior to the freeze event. Adoption of this assessment method would greatly facilitate the characterization and deployment of well-adapted loblolly pine families across the landscape. [ABSTRACT FROM AUTHOR]

Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting. [ABSTRACT FROM AUTHOR]

Human activities have resulted in rapid hydrological change around the world, in many cases producing shifts in the dominant hydrological processes, confounding predictions, and complicating effective management and planning. Identifying and characterizing such changes in hydrological processes is therefore a globally relevant problem, one that is particularly challenging in sparsely monitored environments. We develop a novel, process‐based approach for attribution of hydrological change in such scenarios and apply the approach to the TG Halli watershed outside Bangalore, India, where streamflow has declined considerably over the last 50 years. The approach consists of (a) employing a range of field instrumentation and experiments to identify contemporary streamflow generation mechanisms, (b) using these observations to constrain our understanding and generate hypotheses pertaining to historical changes, and (c) evaluating these hypotheses with a range of evidence including proxies for historical hydrological processes. The body of evidence in the TG Halli watershed indicates the historical presence and subsequent loss of a shallow groundwater table that previously discharged to the stream, meaning that groundwater depletion is the most likely driver of streamflow decline. These findings present a viable path towards improved predictions of future water resources and sustainable water management within the watershed. Our process‐based approach to attribution has the potential to improve understanding of human‐driven hydrological change in regions with poor monitoring of hydrological systems. [ABSTRACT FROM AUTHOR]

Abstract: Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively. [ABSTRACT FROM AUTHOR]

This article describes the field application of small, low-cost robots for remote surface data collection and an automated workflow to support water balance computations and hydrologic understanding where water availability data is sparse. Current elevation measurement approaches, such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies for quantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experiments to evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensors were conducted at three different field sites within the Arkavathy Basin river network located near Bangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolution imagery for topographic characterization, and sonar sensors were deployed on the USV to capture bathymetric readings; the data streams were fused in an automated workflow to determine the storage capacity of agricultural reservoirs (also known as “tanks”) at the three field sites. This study suggests: (i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacity data collected at previously unmapped locations revealed strong power-type relationships between surface area, stage, and storage volume, which can be incorporated into modeling of landscape-scale hydrology. This methodology is of importance to water researchers and practitioners because it produces local, high-resolution representations of bathymetry and topography and enables water balance computations at small-watershed scales, which offer insight into the present-day dynamics of a strongly human impacted watershed. [ABSTRACT FROM AUTHOR]

Accurate pig body weight assessment is an important factor in managing swine herds and imperative when determining acceptable market weights. Pigs marketed outside the desired weight range established by the packer can lead to severe economic loss to the producer. This study's objective was to validate new weight measurement technologies and determine their usefulness on swine farms. Accuracy of three methods were evaluated: human observation, a walk-across platform scale (CIMA; Correggio, Italy), and PigVision mounted cameras (Asimetrix Inc; Durham, NC). Weights were validated with a calibrated livestock scale in all three studies. In the first study, a trained individual selected pigs estimated to be market weight at two sites. Site one had 468 pigs and an accuracy of 84.4%, site two had 522 pigs and an 82.5% accuracy. A 16-week study was then conducted to determine PigVision camera accuracy over time from placement to market. Cameras were mounted above 12 pens. Weights were validated every two weeks. The accuracy for pigs that weighed 32.7 kg (87.7%) was lower (P < 0.05) than the accuracy for pigs that weighed 117.5 kg (97.6%) or 125.7 kg (96.6%). The overall accuracy from placement to market was 94.1%. A final study at market compared human observation, the walk-across scale, and PigVision. A total of 91 pigs were weighed with each method. The accuracy for the walk-across scale was 98.2%. The walk-across scale did not register a weight for six pigs. Final accuracies were 88.2% for human observation, and 96.6% for PigVision. Human observation is the chosen method in many operations today yet offers the lowest accuracy. The walk-across scale is easy to operate but requires tactical animal movement. PigVision is the least arduous option, provides constant data, but does require maintenance. This work was funded by the National Pork Board. [ABSTRACT FROM AUTHOR]

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation. [ABSTRACT FROM AUTHOR]

Vitamin D deficiency appears to be more prevalent than previously considered in the adult critically ill population, and specifically burn-injured patients. No definitive regimen has been shown to restore vitamin D (25(OH)D) levels more effectively to therapeutic levels in the burn-injured population. The purpose of this study was to investigate the effects of either ergocalciferol (D2, 50,000 IU weekly) or cholecalciferol (D3, 6,000 IU daily) in adults with burns ≥ 10% TBSA. This retrospective, observational study (2020-2022) included patients with vitamin D deficiency (< 30 ng/mL) who received replacement and had monitoring with weekly vitamin D levels. Patients on dialysis or those with a hospital length of stay (LOS) less than 2 weeks were excluded. Forty-five patients treated with ergocalciferol and 99 patients with cholecalciferol were included in the study. Patients treated with cholecalciferol were more likely to achieve 25(OH)D levels greater than 30 ng/ml compared to ergocalciferol over a 42-day period (HR 23.56, [95% CI, 12.57-44.16, p<0.0001). A higher proportion of patients in the cholecalciferol group achieved vitamin D greater than 20 ng/ml (HR 6.37, [95% CI, 4.20-9.66, p<0.0001). The adjusted hazard ratios (D3 vs D2) for achieving 25(OH)D levels > 30 ng/ml and > 20 ng/ml were and 23.94 (95% CI 5.09-427.6, p=0.0019) and 7.32 (95% CI 3.83-15.52, p<0.0001) respectively, after controlling for TBSA and initial 25(OH)D. Cholecalciferol appears to be a more effective agent than ergocalciferol for correcting vitamin D deficiency and insufficiency in patients with burn injuries., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Burn Association. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

This work provides a high-level overview of system design considerations for measuring plant architecture traits in row crops using ground-based, mobile platforms. High-throughput phenotyping technologies are commonly deployed in isolated growth chambers or greenhouses; however, there is a need for field-based systems to measure large quantities of plants exposed to natural climates throughout a growing season. High-throughput methods using ground-based mobile systems collect valuable phenotypic information at higher temporal resolutions compared to manual methods (e.g., handheld calipers and measuring sticks). Additionally, the close proximity to plants when using ground-based systems compared to aerial platforms enables plant phenotyping at the organ level. While there is no single best platform for obtaining ground-based plant measurements across crop varieties with different planting configurations, there are a wide range of off-the-shelf systems and sensors that can be integrated to accommodate varying row widths, plant spacing, plant heights, and plot sizes, in addition to emerging commercially available platforms. This chapter will provide an overview of sensor types suitable for phenotyping plant size and shape, as well as provide guidance for deployment with ground-based systems, including push carts or buggies, modified tractors, and robotic platforms., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)