Orchard Robotics Raises $22M to Bring AI to Orchards

Orchard Robotics raises $22M to digitize orchards with AI

Inspired by apple-farming grandparents and guided by Cornell fruit specialists, Charlie Wu left school as a Thiel fellow to build Orchard Robotics. The startup mounts ultra-high-resolution cameras on tractors and farm vehicles, combining computer vision and cloud analytics to give growers a continuous, field-level view of fruit health.

On Wednesday Orchard announced a $22 million Series A round led by Quiet Capital and Shine Capital, with returning backers including General Catalyst and Contrary. The capital will accelerate deployment across apples and grapes and expand into blueberries, cherries, almonds, pistachios, citrus, and strawberries.

The problem Orchard targets is simple and common: growers inspect only a fraction of their trees, so critical decisions — how much chemical to apply, how many workers to schedule, what volume to contract with buyers — are made with rough estimates. Orchard’s cameras capture whole-field imagery as tractors drive, and AI extracts fruit size, color, count, and signs of disease.

The images are routed to a cloud dashboard that turns pixels into a time-stamped record growers can act on. Wu envisions the product evolving from measurement to orchestration — not just reporting what’s in the field, but owning the workflows that follow.

• Real-time yield estimates to plan labor and harvest
• Targeted input application (fertilizer, pruning, thinning)
• Disease and stress detection to prevent losses

Orchard joins a growing set of competitors deploying tractor-mounted imaging, from Kubota-backed Bloomfield Robotics to early-stage Vivid Robotics and Green Atlas. But Wu points to a larger ambition: build an operating system for farms the way Flock Safety expanded from plate recognition to a broader public-safety stack.

The existing specialty-crop data market is modest — roughly $1.5 billion today — yet improvements in model robustness and automation could let image-based systems move from advisory to autonomous decision-making, expanding addressable market to labor management, automated thinning, and precision harvesting.

For growers, the value is straightforward: replace guesswork with measured confidence. Think of it as switching from sample-based polling to full census data for your orchard. That degree of visibility can reduce over-application of chemicals, avoid under-hiring at harvest, and sharpen market commitments.

Organizations evaluating adoption should consider sensor placement, seasonal model drift, and integration with existing farm-management systems. Analytical partners that combine domain expertise in fruit physiology with machine learning and cloud ops accelerate real-world impact — helping translate images into predictable business outcomes.

Orchard Robotics’ $22M raise signals investor belief that specialty crops are ready for data-driven transformation. If the startup can move beyond diagnostics into workflow automation, growers could see yield certainty and cost savings similar to other industries that digitized physical operations decades ago.