Project background
Growers accumulate vast amounts of environmental and imagery data but rarely have the analytics capability to turn it into actionable recommendations. The client wanted an AI layer that could advise on interventions before issues became visible.
Challenge
Training models against a relatively small labeled dataset, handling sensor drift, and ensuring that recommendations were explainable enough for growers to trust and act on. Edge-case behaviors — unusual crops, new lighting — had to fail safely.
Approach & solution
We built a multi-modal pipeline combining vision models for canopy assessment with time-series forecasts of EC, pH, and temperature trends. Recommendations were surfaced with confidence scores and the underlying signals. A feedback loop let growers confirm or reject suggestions, improving the model over time.
Results & benefits
The advisory system flagged early stress indicators days before visible symptoms in several pilot crops, allowing preventive interventions. Grower trust increased once the reasoning behind each recommendation was made transparent.
