Dry patch detection with AI

In rice growing, irrigation is a critical variable. An area with a water deficit can set back the crop's development and calls for a fast response. The problem was not only spotting dry patches, but spotting them in time, every day, across a large-scale production area.

A tool that turns satellite imagery into decision layers. The solution analyzes satellite images of the fields, processes the information with AI models and produces two main outputs: probability heatmaps and georeferenced polygons of likely dry patches.

The results show how applied AI can improve the speed, the coverage and the quality of monitoring in large-scale production processes. 65,000 ha monitored ~3 hours to complete the daily analysis 70% overall F1-score 83% F1-score at early stage