Project background
Growers accumulate years of environmental, imagery, and yield data but rarely have tools to mine it for insight. The client wanted an analytics engine that surfaced what actually drives yield in their specific operation.
Challenge
Unifying data spread across controllers, cameras, and spreadsheets, accounting for confounding variables, and producing insights that growers would trust and act on rather than dismiss as black-box output.
Approach & solution
We consolidated data into a single analytical store, then built feature importance and scenario-comparison tooling on top. Rather than opaque predictions, the engine shows which conditions correlated with best and worst runs, and lets growers explore what-if comparisons across historical harvests.
Results & benefits
Growers identified several previously unrecognized drivers of yield variation — often simple, actionable items like watering timing relative to light. The engine became a regular fixture in weekly operations reviews.
