Kofi Nyarko
Morgan State University, MD·Computer Science
Indoor farms such as greenhouses and vertical farms can grow food close to where people live. Two problems hold them back.
- Fields
Computer Science
Engineering
Plant Science & Agriculture
- Topics
controlled-environment agriculture
vertical farming
greenhouse
- Grants
- 1 known grant, through Jul 2027
What they work on
- Assess the commercial translation potential of an AI supervisory control platform for controlled-environment agriculture, through hands-on investigation of the hydroponic and greenhouse industry.
- Evaluate how computer vision, multi-modal sensors, and continuously adapting machine learning can detect plant stress, nutrient imbalance, and disease risk earlier than manual inspection.
- Examine how AI-decided, human-approved control of pumps, dosing units, lighting, and fertigation (fertilizing through irrigation) can cut the energy use of indoor farms.
More about the research
Crops are often lost because pests, diseases, or nutrient shortages are spotted too late. And running lights, pumps, and climate controls all the time uses a lot of electricity. This work asks whether artificial intelligence (AI) can watch over these farms, catch problems early, and run equipment only when it is actually needed.
Why it matters
Earlier detection of plant stress and disease could reduce catastrophic crop losses in greenhouses and vertical farms, making yields more reliable while using less water and fertilizer. Running energy-hungry equipment only when needed could lower electricity costs. High electricity cost is a major economic barrier for controlled-environment agriculture, so this could make local indoor food production more economically viable.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Javad Mohammadpour Velni
Clemson University, SC·Engineering
Integrate photosynthesis models with real-time biophysical measurements to optimize environmental settings such as light and climate for crop growth in controlled-environment agriculture facilities.
Gundula Proksch
University of Washington, WA·Plant Science & Agriculture
Evaluate aquaponics operations in diverse geo-climatic zones and document how their technologies and practices can be adapted to support local and regional food production.
Somali Chaterji
Purdue University, IN·Computer Science·Early-career
Develop on-device machine learning analytics for farm sensors that adapt to each device's computing power, changing network conditions and competition from co-located applications.
The school
Morgan State University: 3 labs on PI Pages, $56M a year on research, 62 research doctorates a year.
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