Smart irrigation that learns from past seasons

The problem

Common smart irrigation systems all work the same way: if moisture drops below a set threshold, the valve opens. That's a fixed rule with no ability to learn: it doesn't know that one section's soil is sandy and loses water faster, that watering at dawn loses less water than at noon, or that last week's watering was more than the plant could use. As a result, the farmer still has to correct it by hand, and the system performs the same from its first day to its last.

How it works

1. MeasurementMeasuring soil moisture and temperature at several depths, plus the weather and the salinity of the irrigation water.
2. DecisionThe system decides how much water each section needs based on its current model.
3. ExecutionThe valves open, and the flow meter records the amount actually given.
4. FollowingThe system watches how moisture behaves after watering: how fast it drops and how long it lasts.
5. LearningThe system adjusts each section's parameters based on the difference between expected and actual values.
6. ReportA seasonal summary with the water used in each section and how decisions evolved over the season.

Project features

• A closed learning loop: decide → execute → measure the result → adjust the decision.
• The system measures the water actually given with a flow meter; without it, learning becomes mere estimation.
• The system tells sections apart, learning each section's soil behavior separately.
• The system takes the weather into account: watering before a heat wave differs from watering before rain.
• The system explains its decisions, for example: “Reduced the amount by 20% because moisture stayed high for 18 hours after the last watering.”

Expected challenges

These are the main obstacles to expect during the build, laid out clearly so you know them before you start.

• Learning takes many cycles, and real results only appear after weeks; the time required is the project's biggest challenge.
• Cause and effect are hard to link, because the crop is affected by factors other than irrigation, such as disease and fertilizer.
• The flow meter can clog with unfiltered water, and its failure stops the whole learning loop.
• A model learning from little data tends to overreact quickly, so safety limits are needed to prevent extreme decisions.
• Valves and wires installed in the field are exposed to sun, rodents, and plowing.

The consultation gives you a solution for each one.