Case Studies
Edge AI Drone for Orchard Monitoring
Key Results
- 4× Faster Video Processing
- Real-Time Edge Processing (<40 ms)
- 8,000+ Images Used for Model Training
About the project
A global hardware vendor developing an edge AI platform engaged PerformaCode to enable real-time computer vision on agricultural drones. The objective was to process high-resolution video streams directly on the device, avoiding cloud dependency while operating on newly introduced AI-capable hardware.
PerformaCode delivered a production-ready edge AI pipeline, integrating GPU-accelerated inference into a Linux-based environment and adapting pretrained vision models to orchard-specific imagery. The team optimized execution paths for real-time processing and delivered a simplified operator-facing interface suitable for field use.
The resulting system enabled reliable, on-device monitoring of orchard health, supporting timely detection of anomalies and reducing reliance on post-flight data analysis.
7
months
2
engineers
T&M
delivery model
Client challenges
The target hardware platform was new and had limited reference implementations for real-time computer vision workloads. Achieving stable GPU-accelerated inference on an embedded Linux system required careful alignment of drivers, libraries, and framework versions, with little tolerance for runtime instability or performance regressions.
At the same time, the system had to process high-resolution video streams in real time during flight, under strict power and thermal constraints. Cloud-based processing was not an option due to bandwidth, latency, and connectivity limitations in agricultural environments. The client needed a solution that could be deployed and operated by non-technical users, which added pressure to deliver not just working inference, but a reliable, simplified end-to-end pipeline suitable for field use.
Tasks performed
- Prepared and validated the Linux-based AI runtime environment, aligning kernel drivers, GPU support, OpenVINO components, and Python dependencies for stable deployment.
- Integrated OpenVINO inference pipelines for real-time processing of high-resolution video streams captured by drone-mounted cameras.
- Converted and optimized pretrained object detection models using OpenVINO tooling to achieve efficient execution on the target hardware.
- Built end-to-end video analytics workflows, including frame ingestion, preprocessing, inference, and postprocessing stages.
- Prepared and curated training datasets from aerial imagery, including manual and semi-automated labeling of orchard-specific objects.
- Applied data augmentation techniques to improve robustness under varying lighting, altitude, and viewing angles.
- Profiled and optimized inference performance to meet real-time latency constraints under power and thermal limits of the airborne platform.
- Validated pipeline stability and detection consistency through repeated test runs across representative flight scenarios.
- Delivered a simplified operator-facing utility enabling non-technical users to run inference and review results in field conditions.
Project results
4× Inference Speedup
Achieved through GPU-accelerated execution and optimized OpenVINO inference pipelines on the target Linux platform.
<40 ms End-to-End Latency
End-to-end latency reduced by profiling and optimizing preprocessing, inference, and postprocessing stages.
8,000+ Labeled Images
Domain-specific orchard imagery was labeled, augmented, and split into training and validation datasets.
97.2% Pipeline Uptime in Test Runs
Stable on-device inference maintained across repeated test runs through controlled runtime configuration.
Production-Ready Toolchain Delivered
The full pipeline was packaged with a simplified operator utility, enabling field deployment without ML expertise.
Value we bring
Production-Grade Edge AI from Day One
We treat the first working version as a deployable system, not a throwaway prototype. Runtime behavior, data flow, and hardware constraints are validated early, before optimization begins. This shortens the transition from development to deployment and reduces the risk of structural changes late in the project.
Hardware-Aware ML Integration
Our engineers profile and optimize ML workloads in the context of the full system, not in isolation. GPU behavior, memory usage, preprocessing, and postprocessing are treated as a single execution chain. This approach avoids late performance regressions and reduces the cost of tuning after hardware integration.
Operational Toolchains with Full Engineering Artifacts
We deliver ML systems as complete, operable toolchains supported by documented workflows, configuration control, and reproducible execution. Models, code, and runtime components are packaged together with the artifacts required for maintenance and extension. This allows product teams to operate and evolve the system without rebuilding context or reverse-engineering past decisions.
Technologies
- Windows
- Linux
- Python
- OpenVINO
- NVIDIA CUDA
- cuDNN
- TensorFlow
- CNN-Based Object Detection
High code quality, detailed documentation, and fast delivery throughout the engagement.
- Worldwide Silicon Corporation
- Principal Software Engineer, Edge Computing Group
