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Supporting innovative, cutting edge ideas, the Research Innovation Fund (RIF) provides seed grants for cross-college collaborative projects.

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2022
Developing a Smart Feeding System in Fish Aquaculture Systems

Developing a Smart Feeding System in Fish Aquaculture Systems

Feed is one of the biggest production costs when producing fish. A large amount of the feed produced is currently wasted throughout the feeding process. This causes decreased water clarity and affects fish growth rate. During this research project, visual data will be collected along with data from several sensors, to include but not limited to, pH, dissolved oxygen, and temperature. Once data has been collected, a machine learning algorithm will be created to understand when fish should be fed. The goal of this research project is to implement a “smart” feeder within a research setting and gain valuable knowledge about which data is best to understand when fish need to be fed.

COLLABORATORS:
Matt Roohan (CALS); Eugene Won (CALS); Maha Haji (COE)
NMR Measurement of Underground Crops

NMR Measurement of Underground Crops

This project will provide farmers with the ability to estimate the water content (and, consequently, dry matter content) in starchy plants like potatoes and cassava. This will better define the harvest date and allow the growth conditions of the crop to be tuned accordingly. For crop breeders, the success of a new breed can only be determined after the crop has mostly matured (nearly a year for cassava), but with dry matter content measurements throughout the lifecycle of the crop, a breeder can determine the promise of a breed much earlier, perhaps even in the first month. The project depends on how alternating magnetic fields change the orientation of proton magnetic moments in water. A prototype has been created but isn’t yielding results. By adding a second coil, we will troubleshoot and potentially redesign the setup, so we can return a stronger differential output.

COLLABORATORS:
Tangia Sun (CALS); Amal El-Gazaly (COE); Mike Gore (CALS)
Autonomous Monitoring for Offshore Aquaculture

Autonomous Monitoring for Offshore Aquaculture

Aquaculture – with its ability to produce high-quality protein with no need for land, freshwater, or fertilizer – will be key to ensuring adequate food supplies for the world’s growing population. Competition for near-shore space, episodes of poor water quality, and environmental considerations have motivated farmers to shift towards less accessible, more cost prohibitive open ocean farming. Autonomous monitoring can be an effective means of reducing these barriers. Advancements in sensor technologies have allowed for the expansion of remote monitoring in offshore aquaculture farms for water quality and fish behavior. A key requirement of automated offshore monitoring, however, is the transmission of data back to shore. The objective of this research is to design and test an autonomous monitoring system for aquaculture farms that constantly monitors water quality and relays real-time data back to shore via satellite.

COLLABORATORS:
Isabel Mejia-Roberts (UG-COE); Maha Haji (COE); Eugene Won (CALS)
Automated Monitoring of Strawberry Pollination

Automated Monitoring of Strawberry Pollination

The world’s food security is highly dependent on pollinators with bees being primary members of that community. Bees are declining at an alarming rate due to several factors including use of pesticides and disease. This decline has made pollination via drone an appealing alternative. Initial studies on drones’ effectiveness to pollinate strawberry plants has proved inconclusive. This work helps develop additional field experiments to validate current data as well as how this data can be employed in an open-source format. Using automated entrance monitors, solar-powered acoustic sensors, and computer programs that use the size, symmetry, and color of the strawberries to create models. The technology developed for this study can help pollinator focused researchers in the future as they explore methods for monitoring the entrance of a hive, understanding the consequences of pollinator interactions with strawberry plants, and the use of acoustic monitors in a field to measure the presence of wild bees as well as other pollinators.

COLLABORATORS:
Tallisker Weiss (UG-CALS); Kirstin Petersen (COE); Scott McArt (CALS)
Using Real-Time Control and Internet of Things Capabilities to Improve On-Farm Denitrifying Bioreactor Performance

Using Real-Time Control and Internet of Things Capabilities to Improve On-Farm Denitrifying Bioreactor Performance

Agricultural systems need to increase food production to feed a growing population while mitigating the negative impacts of nitrogen fertilizers on water and climate. Nitrogen runoff from agriculture can cause groundwater to have excess nitrogen which can increase greenhouse gas emissions, damages to the ozone layer, acidification of soils and water bodies, and dead zones in coastal areas and estuaries. The goal of this project is to help develop accessible and easily implementable technology using an autonomous woodchip bioreactor and Internet of Things (IoT) to help enable farmers to limit the export of nitrogen to groundwater and surface water ecosystems through real-time data and feedback and help prevent excess nitrogen pollution.

COLLABORATORS:
Sofia Echavarria (UG-CALS); Matt Reid (COE); Scott Steinschneider (CALS)
What Can Rumination Time Tell Us About the Blood Calcium Status of Early Lactation Dairy Cattle?

What Can Rumination Time Tell Us About the Blood Calcium Status of Early Lactation Dairy Cattle?

Subclinical hypocalcemia (SCH) affects over 40% of multiparous dairy cows in early lactation. Arising from a drop in blood calcium (Ca) after calving due to the increased demand for Ca to support lactation. Delayed or prolonged episodes of SCH are associated with decreased production and feed intake, increased risk of additional diseases, and herd removal. Previous reports have shown that daily rumination time (RT) is positively correlated with blood Ca in the early lactation period. As many farms have already implemented sensors to track RT of cows, the results of our study can be incorporated into the preexisting software as an alarm, providing a practical method of identifying cows experiencing delayed or prolonged SCH. By identifying these cows either before or as they are experiencing reductions in blood Ca, more targeted treatment and management strategies can be implemented to reduce the risk of additional disease development, thus improving cow health and productivity as well as farm profitability.

COLLABORATORS:
Claira Seely (CVM); Jessica McArt (CVM); Julio Giordano (CALS)
Exploring Options for Nutrient and Energy Recovery from Aqueous Phase of HTL – a Techno-Economic Analysis

Exploring Options for Nutrient and Energy Recovery from Aqueous Phase of HTL – a Techno-Economic Analysis

Hydrothermal Liquefaction (HTL) is a strong candidate for dealing with wastes, including those from agriculture (e.g. chicken/ cattle manure, food waste, and other lignocellulosic biomass feedstocks) by separating waste into various reusable components. A hinderance for separation on a larger scale is fouling that can occur early in the HTL process. In this research we plan to explore pretreatment options as a step to nanofiltration via membrane separation and related processes. To this end we will explore the option of wet air oxidation in relation to existing technologies that employ activated carbon filtration. Wet air oxidation may allow for the capture of additional nutrients and energy during the aqueous phase (AP) enhancing the sustainability and the development of products of additional value and marketability from HTL of agricultural waste.

COLLABORATORS:
Kalash Pai (CALS); Jefferson Tester (COE); Xingen Li (CALS)
Genetically Encoded Biosensors for Plant Water Stress

Genetically Encoded Biosensors for Plant Water Stress

Creating digital interfaces that allow plants to communicate their needs to humans and automated control systems has the potential to revolutionize agriculture. A system of sensors that can provide a dynamic readout of plant metabolic needs and state of stress is a necessary and currently missing component of this interface. Towards this end, we propose to develop genetically encoded biosensors that provide insight into plant cell stress, including stress related to water deficiency. The proposed sensors are based on a successful design that has been developed for quantifying macromolecular crowding in mammalian cells using FRET (Forster resonance energy transfer) while accounting for spectral interference from the chlorophyll. By overcoming this challenge, we hope this work will help develop sentinel “stress sensor plants” in crop fields can provide a real-time readout of water stress for precise and intelligent irrigation.

COLLABORATORS:
Ling-ting Huang (COE); Matt Paszek (COE); Adrienne Roeder (CALS)
Multi-sensor Crop Monitoring Platform

Multi-sensor Crop Monitoring Platform

This project intends to build a ground-based crop monitoring platform that can analyze crop physiology and canopy characteristics by collecting data during the growing season of maize. Several sensing technologies including a line sensor, an RGB camera, and a lidar will be equipped to the platform to help collect crop physiology data at a high frequency. This platform will be deployed on a maize field of the campus farm where researchers can obtain accurate trends of the maize growth. Currently, the platform works well in a laboratory environment but faces issues in the field due to the effect of the bright sunlight on the ability of sensors to measure crop height at high throughput. The goal is to debug and improve the existing line sensing sensor so that it works on real farms with robustness. If time permits, an automated movement, and navigation system can be added to the platform. This allows the platform to travel straight along a row of maize while collecting data on its own.

COLLABORATORS:
Chenxi Qian (GR-COE); Louis Longchamps (CALS); Joseph Skovira (COE)
Multi-sensor Crop Monitoring Platform

Multi-sensor Crop Monitoring Platform

This project intends to build a ground-based crop monitoring platform that can analyze crop physiology and canopy characteristics by collecting data during the growing season of maize. Several sensing technologies including a line sensor, an RGB camera, and a lidar will be equipped to the platform to help collect crop physiology data at a high frequency. This platform will be deployed on a maize field of the campus farm where researchers can obtain accurate trends of the maize growth. Currently, the platform works well in a laboratory environment but faces issues in the field due to the effect of the bright sunlight on the ability of sensors to measure crop height at high throughput. The goal is to debug and improve the existing line sensing sensor so that it works on real farms with robustness. If time permits, an automated movement, and navigation system can be added to the platform. This allows the platform to travel straight along a row of maize while collecting data on its own.

COLLABORATORS:
Chenxi Qian (GR-COE); Louis Longchamps (CALS); Joseph Skovira (COE)

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