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US (OR): AgTech Innovation Hub announces funding for research projects

The Ohio State University College of Food, Agricultural, and Environmental Sciences (CFAES), Nationwide, and the Ohio Farm Bureau joined forces to establish the AgTech Innovation Hub, a groundbreaking multimillion-dollar research collaboration. The partnership, unveiled during the 2023 Farm Science Review, aims to drive the development of innovative solutions that enhance the agricultural ecosystem while fostering a deeper understanding of and effective management and mitigation strategies for climate risks.

This week marked the selection of the initial research projects that will receive a share of the $2 million in funding pledged by Nationwide to tackle these pressing challenges head-on.

In a highly competitive selection process that included soliciting proposals, evaluating letters of intent, interactive pitch presentations, and meticulous assessment by the Hub Council, two faculty members from the Department of Food, Agricultural and Biological Engineering (FABE) will serve as principal investigators for two of the five funded projects.

Here's a look at Vinayak Shedekar and Sami Khanal's respective projects:

Pulling the plug on drought risks: automated drainage water management
This project will study the effects of automating the drainage water management process using sensor technology that monitors real-time water levels in farmers’ fields. Using an app on their phone or computer, a farmer could program their outlet gates to open, close, and respond automatically to these levels. Data gathered from the sensors could also improve decision-making.

Principal Investigators: Dr. Steve Lyon (School of Environment and Natural Resources, SENR), Dr. Yanlan Liu (SENR), Dr. Vinayak Shedekar (FABE), Dr. Hemendra Kumar (SENR)

AI for farming: harvesting insights from data and drones
This project hopes to use artificial intelligence (AI), machine learning, and drones to simplify the process of identifying crop risks for producers. Instead of using just one type of data to train machine learning models, researchers plan to combine weather and soil data with plant tissue information and satellite, smartphone, and aerial drone images.


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