1. A Fully Scalable Inference System for Daily Orchard Video Uploads
On high-yield days, sometimes processing over 500GB of footage, CtrlCV designed a platform that dynamically spins up multiple parallel virtual machines to maintain fast throughput. On lighter days, it automatically scales down to conserve computational resources. This elastic design ensures consistent delivery speed even when upload volumes spike, delivers reliable performance across unpredictable workloads, and allocates resources optimally to prevent downtime and reduce cloud cost. GreenView now operates continuously at production scale, processing large daily datasets without manual intervention and without overwhelming engineering teams.
Through targeted model optimization and pipeline engineering, CtrlCV achieved a 40% improvement in inference speed. This acceleration enables faster turnaround for growers, reduces cloud compute costs, and improves the system’s ability to scale during peak upload periods.
2. Cloud integrated results with messaging based monitoring
To improve the visibility and oversight, CtrlCV integrated GreenView’s AI results, including the fruit counts, ripeness levels, geospatial mapping, and system health directly into a cloud-managed messaging and monitoring platform. Results are summarised into a communication-friendly format that is searchable, easy to monitor, and capable of automatically flagging anomalies or low-yield areas. Agronomists and authorities gain a clear, high-level overview of orchard performance without navigating technical dashboards, transforming complex analytics into structured, actionable insights.
3. High-Accuracy Fruit Counting at Scale
CtrlCV strengthened Bitwise’s GreenView detection engine with advanced computer vision models capable of handling the real-world complexity of orchard environments. From 2022 to 2024, our systems processed 19,477 km of orchard footage and performed over 3.7 billion fruit detections, enabling tree-level yield estimation across entire orchards. This automated, full-coverage approach replaces traditional manual sampling and delivers forecast accuracy above 70%, giving growers a far more reliable foundation for harvest planning, labour allocation, and logistics optimisation. With accurate counts at scale, growers reduce waste and make more confident operational decisions.
4. Continuous Integration & Continuous Deployment (CI/CD)
In agritech production environments, AI models must evolve without interrupting grower operations. CtrlCV implemented a robust CI/CD workflow which safely rolls out a new detection model, validates the performance of the model using real-world production footage and automatically switches version without any downtime. This approach guarantees uninterrupted service for growers while enabling rapid, safe iteration and continuous improvement. Bitwise can now advance its models at high velocity without scheduling maintenance windows or risking service disruption.