AI in Irrigation How and Why is AI Making Waves in Ag Irrigation?

Discover how AI technology has impacted these 4 areas in agriculture irrigation.

PREDICTING canopy wetness

Environmental InfluenceAI can be applied to growth stage, weather, and soil data in order to foster the develop-ment of more accurate crop growth models, potentially resulting in improved irrigation scheduling.

AI can be applied to learn the associations between available weather, crop and soil condition data, and the corresponding irrigation recommendations of a trained agronomist, thereby automating the repetitive aspects of the scheduling process.

Scheduling & Efficiency

AI can be particularly useful for problems where the underlying physical processes are poorly understood, or difficult to simulate with more traditional models. For example, canopy wetness can be very difficult to diagnose and predict based on scientific principles alone. However, an AI-based model can learn the associations between observed canopy wetness and the factors that influence it. These associations can later be applied to diagnose and predict canopy wetness at other times and locations, without the need for additional sensors.

AI could also be used to automate the analysis of aerial imagery of a field. This might include diagnosing areas of crop stress due to moisture, disease, etc. AI can further identify the relationships between observed crop stress and sensor- or model-based soilmoisture data, helping to fine-tune the trigger points for initiating irrigation.

Taking to the Sky

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WHAT’S NEXT?The time to start collecting data is now in order to realize the many benefits this advanced technology promises.