Modern Agriculture Faces a Data Overload
Imagine every stalk of corn in an Iowa field as a data point. Multiply that by thousands of acres, and add layers of complexity—soil composition, irrigation practices, pest activity, weather forecasts, and more. This accumulation of variables creates what experts call a “data deluge,” leaving many farmers overwhelmed by the sheer volume of information.
Thanks to advancements in precision agriculture, farmers can collect this data more efficiently than ever before. However, interpreting it to make informed decisions is a growing challenge. At The Gazette’s Iowa Ideas conference, agricultural experts discussed how artificial intelligence (AI) is emerging as a powerful solution to help farmers navigate this flood of information.
Artificial Intelligence Offers Actionable Insights
“Over the past decade, we’ve witnessed a convergence of technologies—drones, satellite imagery, and affordable sensors,” said Baskar Ganapathysubramanian, a professor of engineering at Iowa State University. “But farmers don’t just want data—they want actionable insight.”
Ganapathysubramanian, who also leads the AI Institute for Resilient Agriculture, emphasized that AI can offer guidance on issues such as profitability, crop yield, and plant genomics. Rather than navigating endless spreadsheets, farmers could soon rely on AI to interpret the data and suggest optimal strategies.
AI in Plant Breeding and Genetics
Karlene Negus, a graduate research assistant at Iowa State, is exploring how AI can transform plant breeding. Her work focuses on developing models that can predict agronomic traits in corn—such as yield, plant height, and flowering time—based on vast genetic data sets.
“The traditional plant breeding cycle can take eight to ten years,” Negus explained. “AI allows us to shorten that cycle through predictive modeling, enabling breeders to forecast how crops might perform under different environmental conditions or after selective genetic changes.”
New Tools Like InsectNet Empower Farmers
One of the most tangible examples of AI in agriculture is InsectNet, a mobile app developed by Ganapathysubramanian and his team. The app uses AI trained on over 12 million images to identify more than 2,500 insect species with an accuracy rate of approximately 96 percent.
“We trained these large-scale models on supercomputers over several months,” he said. “Now, farmers have another expert in their toolbox—right on their smartphones.”
Farmers can submit photos of insects found on their land, and the app provides quick identification. If the model is uncertain, it says so, rather than guessing. This helps farmers determine whether an insect is a harmful pest or a beneficial species, enabling faster and more accurate field decisions.
Challenges in Data Sharing and Privacy
Despite these advances, AI requires massive data sets to function effectively. One barrier is the hesitancy among individual farmers to share their data due to concerns about privacy, security, and the lack of clear regulatory frameworks. Private companies may also be reluctant to release proprietary data collected from their clients.
“We’re in the big data age,” said Negus. “But there’s a bottleneck in terms of accessible, high-quality data.”
To address this, panelists proposed the use of federated learning, a machine learning method that allows models to be trained across decentralized data sources without moving data to a central server. This method enhances privacy and may encourage more farmers to participate.
Building Trust and Encouraging Adoption
Matthew Carroll, analytics and insight lead for the Iowa Soybean Association, spoke about the importance of trust in encouraging farmers to adopt AI technologies. “Some farmers are early adopters, while others take a ‘wait and see’ approach,” Carroll noted. “That’s not unusual with any new technology.”
Carroll believes that long-standing relationships between growers and trusted organizations like the Iowa Soybean Association are key to increasing AI adoption. “When farmers trust the source, they’re more likely to share data and try new tools,” he said.
Feedback from these early adopters can help developers improve AI tools, making them more effective and user-friendly for the farming community at large.
The Future of AI in Agriculture
As AI technologies continue to evolve, their role in agriculture is expected to grow. From genomics and predictive modeling to pest identification and yield optimization, AI offers a suite of tools that can make farming more efficient, sustainable, and profitable.
Still, the road to full integration isn’t without obstacles. Data accessibility, privacy concerns, and trust remain central issues. But with ongoing collaboration between researchers, industry leaders, and farmers, AI holds the potential to transform the way we grow our food.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.