Archives: Projects

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

Optimization of The Milk Harvesting Process Through Automation and Data Integration

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Adequate udder stimulation of dairy cows before milking is critical for the harvest of high-quality milk, traditionally achieved through milk stripping by hand. To date, dairy operations apply a fixed pre-milking stimulation regimen to all cows, irrespective of their physiological needs. This results in delayed milk ejection (DME) in approx. 25% of cows on New York State dairies. Delayed milk ejection leads to poor milking efficiency, impaired teat and udder health, and reduced milk yield. We estimate that DME results in an income loss of approx. $250,000/year on a 1,000-cow dairy. Accommodating the physiological requirements of individual cows in a precision dairy farming system is of utmost importance to optimal milk harvest and animal well-being. To achieve this, a new dimension for automated identification of cows with DME and providing them with additional pre-milking stimulation using automated pre-milking stimulation (APS) systems is critical.

Real-time Control of On-farm Water Treatment Infrastructure to Enhance Biological Removal of Nonpoint Source Nitrogen Pollution 

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The use of Internet of Things (IoT) tools in agricultural water quality management is a nascent field but holds great promise for innovating decentralized biological treatment systems geared for reducing nonpoint source nitrogen releases from excess fertilizer use. This project proposed to produce a first-of-its-kind testbed IoT-enabled denitrifying bioreactor that will produce novel data on the use of real-time control techniques to enhance nitrate load reductions under real-world conditions. This project aims to retrofit existing edge-of-field denitrifying bioreactors with sensors and actuators to enable real-time control of water levels and labile carbon concentrations in formerly static bioreactors. It will also serve to demonstrate the application of digital tools for agricultural water quality management – and address a critical sustainability challenge for agriculture and food production.

Illuminating Belowground Environments by Harnessing Plant Metabolites for Programmable Plant Phenotyping

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In spite of its potentially massive agricultural and economic impact, state-of-the-art methods remain unable to reliably predict plant performance in real-world field conditions. Much of what is known regarding plant performance in controlled laboratory conditions fails to hold true in real-world environments. To facilitate the translation of “lab knowledge” into functional predictions in the field, we will develop an underground biosynthetic sensor that enables the molecular dynamics of belowground processes to be illuminated aboveground. Biosensors will be used to transmit belowground detection of environmentally-responsive flavonoids into the arial half of the plant where these signals can be detected using automated phenotyping. This project will ultimately produce a new transformative technology for interrogating root-environment interactions. This tool can be engineered to detect almost any below-ground interaction that triggers the production of a specific metabolite (e.g. pathogens, beneficial microbes, and toxic compounds), and thus can be widely adapted to address a range of agricultural challenges.

Intermediaries in Agtech Systems of Innovation: Between Entrepreneurship and Social Impact

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The agricultural innovation landscape is rapidly evolving, and a host of new actors are diversifying a space traditionally dominated by government agencies, public universities, and established agribusiness firms. Globally, investments in agtech start-ups amounted to $30 billion in 2020, representing a 34.5% increase from just the year before (Investable Universe, 2021). Intermediary actors, often referred to as incubators (and accelerators), have arisen in this context to fill a gap in the innovation ecosystem. These incubators often position themselves as mission-oriented – that is, their goals are not only to spawn successful commercial ventures but also to produce positive societal impact by supporting enterprises that contribute to solving social and ecological problems. There is growing interest in metrics, oversight, and reporting protocols to address the challenges of securing and communicating social impact, but these practices are very uneven and very little is known about their performance. This research project stands to generate insights into how entrepreneurs, investors, and innovation intermediaries anticipate, negotiate, and address social impact goals in order to strengthen accountability in innovation ecosystems.

Developing a Smart Feeding System in Fish Aquaculture Systems

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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.

NMR Measurement of Underground Crops

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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.

Autonomous Monitoring for Offshore Aquaculture

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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.

Automated Monitoring of Strawberry Pollination

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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.

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

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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.

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

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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.

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