AI: The key to decoding your data for smarter irrigation

In today’s data-driven agricultural landscape, growers are inundated with a tsunami of information, making it difficult to extract meaningful insights. Even with significant time spent analyzing complex tables and charts, growers are frequently left without clear guidance on what actions to take. This is where artificial intelligence steps in. By turning this overwhelming flood of data into clear recommendations, AI helps growers make quicker, more confident decisions. The result is reduced effort in data interpretation and, more importantly, greater agility, efficiency and profitability in their day-to-day operations.

At its core, irrigation is about stewardship — using water wisely to sustain crops and maximize productivity. It’s not necessarily about using less water, but about using it efficiently: applying it where and when it will have the greatest impact. In practice, however, answering basic questions like “how much water should I use and when?” can be incredibly complex. Growers have access to sensors and services that provide climate, soil, plant, equipment and operational data, often in real-time.

Adopting AI doesn’t mean handing over control of farming operations to a machine.

AI excels at cutting through the noise. By analyzing vast and varied datasets, it brings hidden trends and insights to the surface, empowering growers to make better-informed decisions and focus on what matters most — maintaining productive crops and resilient operations.

To understand AI better, it’s helpful to mention at least two key components: machine learning and large language models. Machine learning is capable of processing data to create predictive models that answer tough questions, and it may even help identify new models and practices that will advance irrigation practices. Large language models, on the other hand, excel at interpreting and summarizing information, delivering insights in plain language. Together, these tools transform raw data into actionable intelligence that guides growers toward more efficient irrigation and smarter overall farm management.

Imagine a grower managing thousands of acres. The right amount of water and fertilizer at the right time means healthier crops, improved yields and optimized input costs. Instead of relying solely on instinct or scrolling through pages of charts and tables, they could use AI to instantly formulate optimal irrigation schedules based on real-time soil moisture readings, forecasted evapotranspiration and planned field operations. With remote pump automation, they could schedule irrigation to run during off-peak energy hours or fine-tune water application with targeted infiltration and minimized runoff.

AI could also flag inefficiencies in the irrigation system, helping the grower address maintenance issues before they become costly problems. Beyond irrigation, AI can aid in choosing crop varieties based on projected water allocations or improving farm operations like pest monitoring, logistics and input cost analysis. Sorting, prioritizing and looking up farm information can be as simple as asking an AI assistant. Bespoke report generation, historical data comparison and even data entry become almost trivial tasks.

However, adopting AI doesn’t mean handing over control of farming operations to a machine. AI is most effective as an advisor, enriching the wisdom of growers, agronomists and irrigation specialists. For instance, while AI might predict when water stress is likely to occur, it’s the grower who ultimately decides how to act on that information, balancing AI’s guidance with on-the-ground realities. This partnership is where AI’s true value lies — not in replacing human judgment but in enhancing it.

AI itself is rapidly becoming commoditized, meaning there’s no shortage of technology providers offering similar features. What sets apart the best is their ability to aggregate and integrate diverse farm data types, spanning irrigation, chemicals, pests and labor, for example, into a unified platform that leverages AI for maximum benefit. Without consistent, accurate data, AI tools cannot deliver the meaningful insights growers want.

Growers should also prioritize providers that foster collaboration and partnerships. For instance, agronomic applications and enterprise resource planning systems working together can reduce duplicative data entry while creating richer datasets for AI tools to analyze. Providers that invest in standardization and robust interoperability will be able to offer growers greater efficiency and deeper AI insights.

Amid the complex demands of running a farm, embracing AI is no longer optional — it’s the key to staying ahead. These cutting-edge tools transform ever-growing data streams into clear, actionable insights, guiding smarter decisions that deliver tangible results. Yes, it may feel unfamiliar at first, but learning to use this technology and providing feedback to providers will aid in making AI more trustworthy, reliable and powerful, paving the way for greater efficiency, better-informed decisions and enhanced ROI for the grower.

Bob Coates is the product manager for water and nutrition at Almanac, a technology provider focused on converting data into agricultural wisdom through its companies and worldwide partnerships.