Our client is a California-based irrigation solution provider dedicated to aiding farmers in optimizing crop yields, conserving water resources, and promoting sustainable agricultural practices. Their smart irrigation solutions empower farmers to make data-driven decisions for improved profitability.
The client sought a machine learning solution to seamlessly integrate with their smart irrigation platform. The primary goal was to accurately predict field capacity based on data acquired from their network of sensors.
Folio3 developed a robust machine-learning model tailored to the client’s needs. This model effectively predicted soil field capacity by analyzing data from various sensors, including soil moisture, temperature, and salinity.
Our solution facilitated the input of sensor data, generated precise field capacity predictions, and provided intuitive visualizations. Additionally, the model enabled AI-driven recommendations regarding water requirements, based on soil sample characteristics.
The implemented model went beyond solely relying on soil sensor data. It also factored in variables such as crop type, weather conditions, soil composition, and additional data.
Consequently, the solution empowered farmers to conserve water, enhance crop yields, and maximize profits. By leveraging the model’s insights, the client successfully augmented their smart irrigation offerings, providing valuable support to the agricultural community.