This is like giving every tree in an orchard its own personal doctor and accountant. Cameras on farm equipment scan the trees, AI counts and measures the fruit, and then tells growers exactly where to act—how to prune, thin, and harvest—to get better yields and more consistent crop quality.
Traditional orchard management relies on manual sampling and gut feel to estimate fruit counts, sizes, and health, which leads to inaccurate yield forecasts, uneven fruit quality, and inefficient use of labor and inputs. Orchard Robotics uses AI-powered imaging and analytics to precisely measure what’s happening on every tree, enabling data-driven decisions for pruning, thinning, spraying, and harvest planning.
Specialized, high-resolution labeled imagery and per-tree orchard data across many seasons and regions, tightly integrated with grower workflows and equipment, which is hard for new entrants to replicate quickly.
Hybrid
Unknown
High (Custom Models/Infra)
On-device or near-edge inference and data bandwidth for processing large volumes of high-resolution images across large orchards; ongoing need for labeled agronomic data across varieties, lighting, and growth stages.
Early Adopters
Focused specifically on permanent crop orchards (e.g., apples, other tree fruit) with per-tree computer-vision analytics, rather than generic field crop precision ag, enabling much finer-grained decisions about pruning, thinning, and harvest at the tree or row level.
109 use cases in this application