Digital Agriculture

Digital Agriculture

Using data, science, and innovation to advance agriculture’s future

What is digital agriculture?

What is digital agriculture?

It’s the use of digital devices to gather, process and analyze spatial (object) or temporal (time) data. This data can then guide targeted actions to improve agricultural efficiency, productivity and sustainability.

It’s the use of digital devices to gather, process and analyze spatial (object) or temporal (time) data. This data can then guide targeted actions to improve agricultural efficiency, productivity and sustainability.

Champions Cultivate the Future

Presenting to the agBOT Challenge judges, Elliott Sass and his Purdue teammates looked far from formal. Stains on their pants mapped precisely where they had leaned against their robot as they made final modifications. Though the team was hard at work for months leading up to the event, they made adjustments until the literal last…

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Director of Agricultural Data Services begins connecting the dots

Barely moved into his new working area, Aaron Walz is already working to bridge gaps between data collection and data use at Purdue. Eager to begin in his new role, Walz has unpacked little more than the essentials: a computer, digital photo frame and his guitar. “We currently have a professor who is working on…

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Speed of data collection takes flight with drones

Digital agriculture developments allow UAVs to collect a new level of data using advanced sensors. Technology opens up grand new possibilities of scale for plant science, transferring what once was slow, inefficient research by humans into rapid collection of vast quantities of data by unmanned vehicles, robotic systems, and sensors embedded in the field.

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Purdue Extension helping Indiana farmers reach new heights with UAV demonstrations

To help Hoosier farmers take full advantage of the promising new UAV technology, Purdue Extension hosted a series of demonstrations throughout the state. Participants received hands-on, practical instruction in flight techniques and learned how to download and process the immense amount of data the drones can collect.
The demonstrations were led by Purdue faculty and Extension specialists, as well as Extension educators and staff from Purdue agricultural research centers throughout the state.

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Digital agriculture improves data yield, yet increases the need for analytics and algorithms

While new facilities and technologies have made data collection easier, digital agriculture creates a new challenge: Once you have the data, what can you actually do with it? That data is rich with useful information for research and industry, but extracting the value still requires tedious human labor. The next frontier for smarter agriculture is developing algorithms that automate these processes, automatically analyzing images and aggregating measurements to produce useful output.

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With digital agriculture, phenotyping improvements are automatic

Jian Jin, Assistant Professor of Agricultural & Biological Engineering, knows to make progress, one must continuously move forward. The mindset is reflected in his automatic high-throughput phenotyping greenhouse, which is able to collect continuous data with hyperspectral imaging. The system is one of several exciting innovations produced by Jin’s lab in the College of Agriculture at Purdue University.

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Milking data to harvest information

Homestead Dairy is a technological leader with thousands of cows, teams of robots, and an overwhelming amount of data. With the advent of digital agriculture, the farm collects data while performing a variety of tasks including milking cows, feeding them, cleaning manure, and even massaging the cows.

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Agricultural economics expert looks for value in digital agriculture

Decisions involving digital agriculture innovations are made with agricultural economics analysis. Nathan DeLay, assistant professor of agricultural analytics and production, focuses on the big picture, making the image clearer for industry. DeLay recently joined Purdue’s Department of Agricultural Economics to focus on data analytics and precision agriculture. His goal is to improve farm policy and producer decision-making.

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