CALS advances strategic plan and innovation through integrated seed grant funding
Twenty-seven grants are for Center for Advanced Innovation in Agriculture affiliated faculty and push boundaries of innovation in agriculture.
A four-wheeled robot roams the diverse terrain of a cow pasture as a drone flies overhead the herd, providing almost real-time modeling and analysis of the robot.
Using the data provided from the drone and animal and environmental sensors, the robot performs management tasks, demonstrating the capabilities of an integrated suite of technologies to monitor pollutant hotspots, soil and water characteristics, and cattle movement in pastures.
“The goal of this research is to build a suite of affordable, small-scale technologies for use on small- and medium-sized livestock operations to facilitate meaningful improvements in rural land management and agricultural water quality, and ultimately a more resilient agricultural landscape,” said Zach Easton, a professor of biological systems engineering, Virginia Cooperative Extension specialist, and principal investigator of the project.
Through the improved capacity to study and understand the relationship between cattle, soil, forage, and water in pasture systems, the research can help design management practices to minimize runoff of harmful pollutants into sensitive waterways.
Easton’s grant was one of 32 that the College of Agriculture and Life Sciences recently awarded to researchers around the college who are examining everything from robotics to pheromone sampling. The seed grant program supports the priorities of the 2020 CALS strategic plan. Twenty-seven of the grants were awarded to affiliated faculty in the Center for Advanced Innovation in Agriculture (CAIA).
The recently formed center in the college is a catalyst for research that spans disciplines in order to advance technologies and enhance decisions for expanding agricultural and food systems. CAIA-affiliated projects are composed of teams across agriculture and life sciences academic units as well as data analytics and engineering, including faculty from other colleges, to tackle the big challenges and future opportunities in agriculture and food systems. The center will help cement Virginia Tech’s role as a comprehensive and innovative global research leader in smart and secure agriculture technologies and data analytics for informed decisions.
“The ‘Eyes in the Sky and Boots on the Ground’ project is an example of developing the Internet of Things applications to agriculture. By funding these innovative research projects, the center is fulfilling its goal to create an agile and responsive network of interdisciplinary researchers, transdisciplinary teams, and Virginia Cooperative Extension specialists who are charting the course for the future of agriculture,” said Susan Duncan, the center’s director and the associate director of Virginia Agricultural Experiment Station.
Song Li, an associate professor in the School of Plant and Environmental Sciences and affiliated faculty member of the center, is using the grant the college awarded him to study how machine learning can assess specialty crop health and quality.
Specialty crops have higher profit margins than traditional row crops and are a vital economic crop for Virginia and nearby regions. These crops are also well-suited to smaller-scale farms.
Through this research, Li and his team aim to develop new machine learning models that can be used by producers. The final product of this research is a portable device that includes a mobile phone application, a spectral filtering lens, and a miniature microscope that can be fitted onto a cell phone to collect data and make decisions automatically in the field. This example of artificial intelligence development for agriculture exemplifies opportunities for influencing agricultural decisions and driving the bioeconomy.
Another of the funded projects, “Using imaging for animal health and management,” focuses on the early detection of mastitis, an intramammary gland infection, in dairy cattle and has well-recognized detrimental effects on animal wellbeing and dairy farm profitability.
Early detection of both mastitis and pregnancy establishment allows dairy farmers to intervene quickly, provide health care, and implement management strategies to ensure maximum animal wellbeing and productivity.
“Our multidisciplinary and innovative research explores noninvasive and automated systems that can leverage hyperspectral imaging, robotics, and machine learning to detect and diagnose the early onset of subclinical and clinical mastitis and pregnancy establishment in dairy cattle,” said Vitor Mercadante, an assistant professor in the Department of Animal and Poultry Sciences, Extension specialist, and principal investigator of the project.
Mercadante and his team will develop a robotic sensor platform for animal health and production management, test the sensor platform by collecting controlled sample images, and develop a machine learning method for automatic classification of hyperspectral images to identify disease and non-diseased, and pregnant and non-pregnant cows. These technological innovations can sustain animal health, allow for early detection of animal illness, protect against loss of value, and support a continuous food supply.
Other projects that were funded include:
- Glenda Gillaspy, Department of Biochemistry, “The Phosphorous Crisis.”
- David Schmale, School of Plant and Environmental Sciences, “Drones and Precision Weather.”
- Biswarup Mukhopadhyay, Department of Biochemistry, “Efficient Anaerobic Digestion of Food Waste to Methane.”
- Haibo Huang, Department of Food Science and Technology, “Dietary Fiber to Modulate Gut Microbiome.”
- Nicholas Santantonio, School of Plant and Environmental Sciences, “High-Throughput Phenotyping for Malt Quality.”
- Ryan Stewart, School of Plant and Environmental Sciences, “Low-Cost, Distributed Co2 Sensing.”
- Robin White, Department of Animal and Poultry Sciences, “Automated Meat Processing.”
- Bo Zhang, School of Plant and Environmental Sciences, “Quantification of Trypsin Inhibitors.”
- Vijay Singh, Eastern Shore Agricultural Research and Extension Center, "Integrating UAS and Harvest Weed Seed Control Strategies."
- Albert Auguste, Department of Entomology, “A Novel Vaccine for Cache Valley Virus.”
- Brian Badgley, School of Plant and Environmental Sciences, “Plant-Microbiome-Environment Interactions.”
- Ksenia Onufrieva, Department of Entomology, “Novel Pheromone Sampling.”
- Hasan Seyyedhasani, School of Plant and Environmental Sciences, “Remote Human-Robot Collaboration System.”
- Alejandro Del Pozo-Valdivia, Hampton Roads Agricultural Research and Extension Center, “Remote Sensing in Virginia Nursery Crops.”
- Mark Hanigan, Department of Dairy Science, “Sl-Dairy: Precision Feeding and Diagnostics.”
- Mizuho Nita, Department of Alson H. Smith Jr. Agricultural Research and Extension Center, “New Technologies to Assess Grape Disease Risks.”
- Mark Reiter, Eastern Shore Agricultural Research and Extension Center, “Precision Ag For Water and Mites.”
- Carl Stafford, Virginia Cooperative Extension, “Graze 300 VA.”
- John Ignosh, Department of Biological Systems Engineering, “A Better Solar ‘Panel’ from Virginia Tech.”
- Clay Wright, Department of Biological Systems Engineering, “Decoupling Growth and Nutrient Signaling.”
- Gillian Eastwood, Department of Entomology, “Tick-Borne Viruses in Virginia.”
- David McCall, School of Plant and Environmental Sciences, “Precision Turfgrass Management Adoption.”
- Reza Ovissipour, Virginia Seafood Agricultural Research and Extension Center, “Insect-Based Packaging Film.”
- Bingyu Zhao, School of Plant and Environmental Sciences, “The SyFu System.”