Field Capacity Prediction
Folio3 developed a complete machine learning model
Folio3 developed a complete machine learning model to accurately predict field capacity based on soil moisture and water content data captured by sensors for a California based irrigation management solutions provider.
The client was a California based irrigation solution provider that helps farmers improve overall crop yields, optimize water usage, and sustainably preserve other scarce resources. Their solutions enable farmers to deploy smart irrigation management solutions that help them improve their bottom line.
The client required a machine learning solution/model to work with their smart irrigation platform to enable them to accurately predict the field capacity of soil based on readings from their deployed sensors.
Folio3 developed a machine learning model for the client to accurately predict the field capacity of different soil samples using data collected from sensors ranging from soil moisture, temperature, and salinity.
Folio3’s solution helped them feed data into the model, generate predictions for field capacity for the data, and visualize the information. The model enabled the solution to produce AI-based recommendations on the water requirements of a certain area based on the soil sample.
Ultimately, the model took in data from the soil sensors, but also crop type, weather, soil type, and additional data to enable the solution to help farmers save water, increase crop yields, and maximize profits.