Building the Next Generation of Farm Technology
Agriculture is no stranger to innovation.
Over the past several decades, farmers have adopted GPS guidance, precision planting, advanced sensors, drones, and increasingly sophisticated equipment. Now, artificial intelligence and automation are becoming the next major layer of that evolution.
But as Harman Sangha explains on The Germinate Podcast, building the next generation of farm technology isn't simply about adding more AI, sensors, or automation to equipment.
It is about building technology that solves real problems for farmers and actually works when they need it most.
From the Field to the Lab and Back Again
Harman's path into agricultural engineering has taken him through startups, universities, research labs, and farm fields across multiple countries.
After earning his bachelor's degree in agricultural engineering in India, he worked with an ag startup developing handheld cotton pickers for small-scale farmers. He later studied UAVs, multispectral cameras, and thermal sensors at Kansas State University before completing his PhD at Iowa State University, where he focused on machinery automation and worked with industry partners on next-generation agricultural systems.
Today, as an assistant professor at Auburn University, his work continues to center on one important question:
How can we use automation, computer vision, and AI to solve real problems for farmers?
Why Agriculture Needs AI
The pressure on agriculture continues to grow.
Food demand is increasing while available cropland is becoming more limited. Producers are being asked to generate more output from the same or fewer resources.
Harman believes machine learning and AI can help agriculture find efficiencies that are difficult to achieve with mechanical systems alone. Even a small improvement in efficiency can become significant when applied across thousands of machines and millions of acres.
Modern agricultural equipment also generates enormous amounts of data. Machine learning can analyze those inputs, identify patterns, and help equipment make better decisions in situations where a human operator could never manually process every variable.
Precision Is Getting Even More Precise
Planting is one example of where this technology could make a difference.
Agriculture has already become incredibly precise about factors like seed spacing and depth. Harman discusses research that goes even further, looking at details such as seed orientation and how the position of a seed in the soil could influence germination.
Machine learning could potentially analyze factors like ground speed, soil moisture, vacuum pressure, and equipment settings to better understand the conditions that produce the best results.
These may seem like small details, but they could represent the next level of precision agriculture.
Research Has to Solve Real Problems
Harman's approach to research starts with the people who will actually use the technology.
Rather than developing something in a lab and then searching for an application, he talks with farmers and extension agents about what isn't working.
Where is current machinery falling short? What takes too much time? What could be more efficient?
Then the research can focus on solving those problems.
Harman wants to develop systems that can move from research into practical applications relatively quickly. His current work includes projects with peanut and cotton farmers, along with research exploring whether technology could help shorten the process of developing new crop varieties.
Automation Could Help Solve Agriculture's Labor Problem
When Joe asks where Harman sees the future of agriculture going, his answer is clear: automation.
Labor shortages affect nearly every part of agriculture, from harvesting and spraying to fertilizer application and specialty crop production.
The problem becomes especially significant during narrow operating windows. A farmer may only have a few days to harvest before weather conditions change, and every other farmer in the area may be working against that same deadline.
There may be plenty of equipment available, but not enough trained operators to run it.
Automation can help close that gap.
New Technology Still Has to Work
There is another side to automation that can easily get lost in the excitement surrounding AI.
What happens when the technology breaks?
For farmers working within extremely tight planting or harvest windows, waiting for someone to diagnose a complicated software problem isn't always an option.
That is why Harman believes the next generation of farm technology needs to focus on robustness as much as innovation.
Farmers need automated systems they can trust. If something does go wrong, they need a way to fix it quickly enough to keep moving.
The most advanced machine in the field isn't very useful if a farmer cannot depend on it when it matters most.
Building the Next Generation of Ag Engineers
Developing better technology also means developing the people who will build it.
Agriculture increasingly needs engineers who understand crops and machinery but also have skills in software, hardware, machine learning, and automation.
Harman believes universities have an important opportunity to bridge that gap.
Agricultural and biosystems engineers already understand the unique challenges of farming. Giving those students stronger skills in AI and automation could prepare them to develop technology specifically for agricultural applications.
It also opens opportunities beyond the biggest names in agriculture. Smaller equipment manufacturers and ag startups need engineers capable of bringing automation into specialized machinery, and young engineers may have the opportunity to make an immediate impact in those organizations.
Build for the Farmer First
For all the excitement around artificial intelligence, automation, machine learning, and precision agriculture, Harman's philosophy comes back to something much simpler.
Start with the farmer.
Understand the problem. Build a solution. Make it reliable. And make sure it creates enough value that someone will actually want to use it.
Harman says one of the things he enjoys most about agricultural engineering is getting out into the field, putting on his steel-toe boots, working directly with machinery, and building something that could benefit farmers within the next few years.
That may ultimately be what defines the next generation of farm technology.
Not who can build the flashiest AI system or add the most features, but who can create technology that makes farming more efficient, reliable, and productive when it matters most.
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