Mississippi soybean [Glycine max (L.) Mer.] producers are under pressure to plant as much land as possible within narrow planting windows. The 5-year average planting progress is 45% of the total at the end of the optimal soybean planting window. New metering and seed delivery technology claims faster planting without sacrificing singulation, stand, or yield, but these tools need to be validated before recommendation. This study aimed to quantify soybean response to planting speeds using a precision planter (John Deere MaxEmerge 2 row units retrofitted with Ag Leader SureSpeed and SureForce) and a mechanical planter (John Deere 1700 ground-driven mechanical planter equipped with eSet meters) for a total of 7 site-years across Mississippi. In 2022, both planters were evaluated at four actual ground speeds of 7.9, 10.8, 13.5, and 16.4 km h-1 in a 2 x 4 factorial design. The experimental design was modified in 2023, where the mechanical planter served as the current farmer practice check at 9.7 km h-1and the precision planter speeds were 9.7, 14.5, and 17.7 km h-1 at research station sites and 9.7 and 14.5 km h-1 at an on-farm site. Across sites, increased planting speed generally increased plant spacing, in-row spacing variability, and decreased plant population. However, increased speed did not affect soybean yield. The precision planter at 17.7 km h-1 was no different from the mechanical planter in terms of soybean plant population, spacing, and yield in 2023. Results suggest soybean producers can plant soybean at 17.7 km h-1 without compromising yield. Planting within optimal planting dates maximizes soybean yield. Planting faster results in more area planted during ideal planting dates. No yield difference was observed when planting up to 17.7 km h-1 during 7 site-years. Reduction in stand occurred with faster planting speed but was never yield-limiting.

Ocean and coastal water quality monitoring has become more important in recent decades as onshore anthropogenic activities contribute more pollutants to surface water runoff that flows to offshore systems like the Gulf of Mexico (GOM). Many coastal systems are major sources of income and natural resources for human populations. The Mississippi Sound (MSS) is one such system and can be characterized as a large, nutrient rich, coastal estuary in the Northern GOM. The MSS is bounded by the mainland coast of Mississippi to the north, Mobile Bay to the east, Lake Borgne (Louisiana) to the west, and a series of barrier islands (Cat, Ship, Horn, Petit Bois, and Dauphin Islands) to the south. Major freshwater inputs to the MSS are the Mobile River (via Mobile Bay), the Pascagoula River (via Pascagoula Bay), the Pearl River (via Lake Borgne), and on occasion the Mississippi river (Bonnet Carre spillway openings). Minor inputs are the Biloxi, Tchoutacabuffa, Wolf, and Jourdan, rivers. The barrier islands present an obstacle to water exchange between the MSS and the GOM suggesting prolonged weather or anthropogenic events (i.e., hurricanes or spillway openings, respectively) can greatly impact the biota (e.g., algae) and reliant ecological processes of the MSS due to reduced water exchange with the GOM. Water quality of the MSS is dynamic and can change seasonally, thus, a baseline understanding of water quality and algal density across the MSS can aid resource managers by providing a better understanding of freshwater particulate and solute impacts to the MSS over time. Algae is typically measured via pigments like chlorophyll A (universal algal pigment), phycoerythrin (restricted to saltwater cyanobacteria), or phycocyanin (restricted to freshwater cyanobacteria) which can contribute to water turbidity and therefore affect plant and fish growth. Water quality (DO, CDOM, Temperature, Turbidity, pH, and Salinity) and algal metrics were characterized spatially in the MSS in 2023 after autonomous surface vessel data collection. Data were used to assess water quality and algal metrics from the mainland coast to the offshore barrier islands of the MSS as well as across the MSS from LA to AL. A generalized linear model (GLM) was used to detect differences among transects. Algal metrics were correlated to water quality metrics as well. In general, all metrics were significantly impacted by proximity to the mainland coast and proximity to major freshwater inputs (Pearl and Pascagoula systems). Algal metrics were correlated more strongly with turbidity, CDOM, pH, and salinity than temperature or dissolved oxygen suggesting that these four metrics may have more influence over algal density than the others. Data collected by ASV was positively correlated with data collected by manned vessel. However, not all ASV collected data could be used as a surrogate for data collected from a manned vessel as dissolved oxygen and pH differed among the two datasets while CDOM, temperature, turbidity, and salinity were equivalent among the two. This work highlights the utility of ASV collected data as a viable option for stakeholder decision making purposes and suggests this technology can be effectively utilized to supplement (and possibly replace) existing monitoring techniques. (Abstract)

With the increasing need for real-time monitoring and management of aquatic ecosystems, autonomous surface vehicles (ASV) are a vital tool for in situ water quality sampling. Compared to conventional monitoring methods, solar powered ASVs (SP-ASV) serve as mobile monitoring stations equipped with various sensor platforms to enhance spatio-temporal water quality data collection by providing extended-duration, continuous monitoring of diverse ecosystem. The SeaTrac SP-48 is ASV designed to surface navigate varying aquatic ecosystems. The SP-48’s solar power system, navigational dashboard, payload capacity, and sensor agnostic design provide capabilities to host and power a variety of sensors for multi-task over-the-horizon operations to persistently and autonomously monitor aquatic ecosystems for extended durations of time.The objective of this research was to evaluate the energy budget (energy production/consumption) of a commercially available multi-purpose SP-ASV (SeaTrac SP-48) equipped with an integrated payload package of environmental monitoring sensors during an extended-duration mission. For this study, operation of the integrated sensors package and SP-48 navigation system resulted in a mean power load of 0.14 kW. Due to decreasing solar energy potential over the 29-day period (photoperiod (PD), sun angle, and weather conditions), energy consumption was slightly higher than energy production.The results of this work will benefit ASV operators when planning missions in order to manage an energy budget. The results will also help researchers and ASV operators optimize energy consumption. Future work should assess ASV energy budget in summer months as photoperiod and solar irradiance are at peak levels to establish peak performance baselines

The need for real-time monitoring and management of water quality in inland and coastal marine environments is increasingly significant due to increases in land utilization which can negatively impact aquatic ecosystems from surface water runoff. Conventional water quality monitoring methodologies are laborious and expensive, requiring in situ monitoring stations and/or specialized manned vessel sampling missions at fixed locations and resultant laboratory analysis of water samples. These conventional methods are limited in their ability to gather high resolution spatio-temporal data. Multi-purpose autonomous surface vehicles (ASVs) provide a powered platform for sensors/instrumentation and serve as mobile sampling stations that enhance spatial and temporal data gathering capabilities. Solar powered ASVs provide long endurance continuous operations capabilities. However, commercially available solar powered ASVs are limited, and ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated the ability of a commercially available solar powered ASV equipped with a multi-sensor payload to operate autonomously to accurately and repeatedly maintain established AB line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). Mean XTE did not exceed 2.39 m. This work provides a conceptual framework for development of spatial and temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of obstacle avoidance and adaptive sampling technologies.

Autonomous Surface Vessels (ASVs) are useful tools for monitoring and management of waterbodies to increase data capture rates and quantities within shorter time frames and at lower costs than manned methods. SeaTrac Systems Inc.’s SP- 4S ASV is an autonomous boat designed to provide a platform to collect water quality data on a long term (i.e., months) basis. Solar panels provide continuous power supply to the vessel and the instruments within. Autonomous steering and path tracking capability of the ASV allows users to predetermine a data collection path using Geographic Positioning System (GPS) waypoints. SP-48 is designed to collect water quality parameters that include Chlorophyll a (Chl-a), Phycocyanin (PC), Phycoerythrin (PE), Colored Dissolved Organic Matter (CDOM), Dissolved Oxygen, Temperature, Turbidity, Salinity, pH, Partial Pressure of Carbon Dioxide (pCO 2 ), and Backscattering. To provide collection of these parameters, six water quality sensors (SeaBird Scientific Inc.’s ECO-Triplet-FL3-B [Chl-a, PC, PE], ECO-Triplet-BB2FL [CDOM, Turbidity], ECO-Triplet-BB3 [Backscattering], SBE 63 [Dissolved Oxygen], ProOceanus Inc.’s CO 2 ProCV [pCO 2 ], and AML Oceanographic Inc.’s CT Xchange [pH, Salinity, Temperature]) were integrated into the ASV. Additionally, the ASV has integrated instruments that capture GPS, wind, and meteorological data. The GPS is captured by an Airmar GH2183 Network GPS Compass and the wind and meteorological data is captured by an Airmar 200WX WeatherStation. To receive the data from the water quality sensors, two options were considered. One possibility was to use sensor specific software to capture and store the data on the ASV onboard computer. This would require all software to run every time the ASV is deployed. In addition, the output files would not be accessible for real-time processing, preventing real-time data visualization and monitoring. The second option was to create a single interface to obtain data from all the sensors and send it to a server on a real-time basis. Ultimately, a connection tool (named Sensors Bridge) was developed to transmit water quality, GPS, and meteorology data by capturing information from communication (COM) ports and a LAN port onboard the ASV. The captured data was sent to a server located at Mississippi State University via a cellular network for storage and visualization. A Node.js server was created to provide the gateway to the database and the publicly available web application. The Node.js server processes the raw data, converts it to its final form, and saves it to the database. The data was stored in a PostgreSQL/ PostGIS relational spatial database. The web application (web app) named Water Quality Monitor was developed to visualize data in real-time as well as query historical data. The app contains four major components (Dashboard, Charts, Maps, and Add Location) that are accessible through a tabs interface in the app. The Dashboard component displays the last 30 records captured for each water quality parameter as a line graph depicting parameter magnitude as a function of time. Additionally, it displays the current location of the vessel and recently recorded points. There is also a bar graph showing all the parameters with the number of data points stored in the database, which can help monitor the quantity of records stored in the database for each parameter. The Chart tab assists in querying and visualization of historical data as a line or bar graph. Data can also be downloaded in image and spreadsheet formats. The Map tab provides the option of visualizing the water quality data spatially as a raster, vector, and heatmap. Users can download the data as a spatial file format. The Add Location tab enables system administrators to add a new study area. Once the boundary, name, and code of a study area are specified, database tables are automatically created. When the ASV starts capturing data from a study location, the data is saved to its respective locational database tables. Complete implementation of real-time data capture and visualization streamlines water quality monitoring. Additionally, captured data can be used for time series analysis. The current implementation lays a foundation for a decision support system.

Water quality monitoring is becoming increasingly important as human populations grow, industrial and agricultural activities expand, and climate change threatens to cause major alterations to the hydrologic cycle. With advent of new technology, autonomous surface vessels (ASVs) are able to provide data with high spatial and temporal resolution, which is critical for water quality monitoring and management. A suite of sensors has been integrated in a novel solar-powered ASV that can trave1 $\sim$ 9.26 kmph or can be stationed at a location collecting continuous data. The overarching objective of this paper is to present the efficacy of the ASV in collecting accurate water quality data by comparing these data with data from another set of independent sensors as well as laboratory analysis of water samples. In-situ water quality data from selected sites together with ASV data were collected from four study areas in Mississippi, USA. Salinity, temperature, pH, and dissolved oxygen (DO) measured by the ASV were compared with the measurements by a profiling sensor suite and ASV measured chlorophyll a, phycocyanin, colored dissolved organic matter (CDOM), turbidity, and partial pressure of carbon dioxide (pCO 2 ) were compared with the measurements from laboratory analysis of water samples collected from the same location and approximately at the same time as the ASV measurements. The comparisons produced correlation coefficients of 0.999, 0.985, 0.974, 0.755, 0.701, 0.633, 0.755, 0.839, and 0.999 for salinity, temperature, pH, DO, chlorophyll a, phycocyanin, CDOM, turbidity, and pCO 2 , respectively and the root-mean-square deviations for salinity, temperature, pH, DO, chlorophyll a, phycocyanin, and pCO 2 were 1.29 PSU, 0. $671{}^{\circ}\text{C}, 0.56,3.1\text{S}\text{m}\text{g}/\text{L}, 1.12\mu \text{g}/\text{L}$, 0.694 $\mu \text{g}/\text{L}$, and 18.8 $\mu \text{a}\text{t}\text{m}$, respectively. This ASV, along with its sensor suite, should be valuable for water quality modeling and management due to its potential to provide large amounts of accurate data.

Providing a suitable lighting environment for commercial poultry is essential for physiological processes, ensuring welfare, and achieving production goals. Recent research has shown that poultry house illuminance (intensity) is dependent on ambient sunlight conditions and that control of light leakage can improve live performance. The objective of this study was to evaluate the performance of fan shades for reduction of light leakage and spatial variation of illuminance in commercial broiler houses. Data collection systems were developed to measure whole house illuminance at a fixed point in time (static) and over a 24-h period (temporal) in a house with and without fan shades. Results from static and temporal testing indicate that fan shades significantly reduce light ingress through tunnel exhaust fans and result in reduced whole-house mean illuminance. Whole-house illuminance uniformity was significantly lower (P = 0.0024) in houses with fan shades (CV = 137.4) than those without (CV = 280.8), and an additional 1.25 h of target illuminance levels (#10 lx) were gained on the day of testing in houses with fan shades installed. Overall, results indicate fan shades are an effective strategy to mitigate light ingress through fans and improve illuminance uniformity within commercial broiler houses.