How to make every seed count when planting crops

In modern agriculture, accurate seed singulation and population counting are vital to achieving high crop yields and avoiding the significant cost penalties of missed seeds (skips), multiple seeds (doubles), and population errors. ROJ integrates edge AI directly into the seed metering unit with its Vision4Seeding platform, using the STM32N6 MCU to detect seed status in real time at 38 inferences per second. Operators get automated visibility on every row, with no cloud dependency.
 

Application domains:

• Precision seed planting
• Smart farming and AgTech
• Agricultural machinery (OEM and retrofit)

As farms grow larger and the variety of crops increases, the whole industry shares the need for more precise seed planters. Accurate singulation, meaning exactly one seed placed at each position, directly impacts crop yield and farm economics.

Traditional solutions struggle to meet these demands, especially in multi-row, high-speed operations with diverse seed types. On planters with over 40 rows and discs spinning at 30 to 40 rpm, the task becomes significantly more complex. Seed sensors on tubes fail with small or irregular seeds and often miss or double-count. Cameras on seeding discs often deliver low-resolution or blurred images at high seeding speeds, and still rely on the operator's eye and manual adjustments. Both approaches fall short of the precision modern agriculture requires.

Vision4Seeding changes this by integrating edge AI directly into the metering unit. Machine vision algorithms running on the STM32N6 continuously analyze high-speed camera feeds from each planter row, detecting skips and doubles and counting seeds in real time.

Key benefits of this edge AI approach:

• Automated real-time monitoring: AI continuously detects planting errors, so operators no longer need to watch camera feeds
• High precision and consistency: Errors are detected even at higher RPM, independent of lighting and operator fatigue
• No cloud dependency: Inference runs on the device, minimizing latency and network requirements
• Bandwidth-efficient: Only relevant snapshots and compact inference results are sent to the operator interface
• OEM and retrofit compatible: A flexible path for both new and existing machinery

The Vision4Seeding system integrates a global shutter camera with dedicated illumination, mounted directly on the metering unit.

Each cycle begins with a synchronization signal from the seeding disc, which triggers image capture so that the new seeds are in frame.

Snapshots are processed in real time by an optimized neural network on the STM32N6, accurately detecting seeds, skips, and doubles even at high speeds and across diverse seed types.

Snapshots are compressed to JPEG and sent, together with the inference results, via Ethernet to the operator interface for centralized monitoring of multi-row planters. Sending only relevant images and compact inference data ensures precise visibility while minimizing bandwidth usage.

Vision4Seeding relies on the AI acceleration of the STM32N6 (Neural-ART NPU) to run object detection on every capture cycle. With an inference time of 26 ms, the system sustains 38.4 inferences per second, keeping pace with high-speed planting operations where the disc spins rapidly and image capture must stay synchronized with disc rotation.

Global shutter color sensor: VD66GY

The VD66GY captures sharp, coherent color images at high speed, avoiding motion distortion even when the seeding disc is spinning rapidly. This ensures reliable input for the detection model across operating speeds.

• STM32Cube AI Studio, for neural network conversion and deployment
• ST Edge AI Core command-line interface
• STM32CubeIDE, for application development and debugging
• STM32CubeProgrammer, for flashing the artifacts to the target

• ST Edge AI documentation homepage