LiDAR in oogstsystemen: betrouwbare detectie onder zware veldomstandigheden

LiDAR offers the most added value in harvesting systems when dealing with:

• reliable detection under dust, vibration, and varying light
• precise positioning with respect to crop and machine environment
• Stable obstacle detection during dynamic field situations
• preparation for semi-autonomous and autonomous functions

Why LiDAR Adds Value in Harvesting Systems

Within harvesting processes, a machine must continuously react to what is happening directly in front of, beside, or underneath the system. Crop rows must be tracked correctly, obstacles must be identified in a timely manner, and machine parts must be accurately positioned relative to the crop. As soon as sensor data becomes unstable under these conditions, it directly impacts the machine's performance.

LiDAR is precisely strong in such environments because the sensor measures actively and is much less dependent on light, contrast, and image quality. This makes the output more stable when field conditions change rapidly. For machine manufacturers, this means systems can function more consistently, even when dust levels increase, light conditions change, or crop structure varies significantly.

An important difference from simpler detection principles is that LiDAR not only detects presence, but also accurately maps out distance, shape, and position. As a result, the technology is well-suited for use in harvesting applications where real-time insight is needed to reliably control machine functions.

Why LiDAR and not just cameras?

Cameras are widely used in agricultural machinery and can add significant value, for example for visual recognition, classification, and operator support. However, in the practice of harvesting systems, camera-only solutions have clear limitations. Light conditions in the field are rarely constant, and environmental contamination is the rule rather than the exception. This makes image interpretation vulnerable at precisely those moments when reliability is most important.

Limitations of camera-only solutions:

• performance decreases in bright sunlight, shade, and twilight
• Dust, mud, and crop residue interfere with image recognition
• distance and depth information requires extra processing
• greater probability of error with varying crop heights and structures

LiDAR takes a different approach. The sensor provides direct 3D distance information and is much less dependent on light and contrast. This creates a more stable foundation for functions such as obstacle detection, crop tracking, positioning, and volume measurement. This makes the technology particularly suitable for applications where a machine must continue to function reliably under harsh and fluctuating conditions.

Why LiDAR is often stronger here:

• Direct and accurate 3D distance information
• less dependent on lighting conditions and contrast
• more stable detection under heavy field conditions
• strong foundation for automation and autonomy

This doesn't mean cameras don't play a role. In modern harvesting systems, cameras and LiDAR complement each other well. Where cameras provide visual context, LiDAR provides reliable geometric information. In combination with, for example, GNSS and other sensors, this creates a more robust overall concept with fewer chances of failure in the field.

Where LiDAR is used in harvesters

The added value of LiDAR lies not in a single function but in multiple parts of the harvesting process. An important application is tracking crop rows and positioning the machine relative to the crop. By continuously measuring structure and position, a machine can work more accurately and respond better to variations in the field. This helps to limit crop damage and better align machine functions with the current situation.

LiDAR is also very suitable for obstacle detection. In a harvesting environment, unexpected objects such as poles, crates, people, implements, or terrain variations can directly affect safety and continuity. LiDAR makes it possible to detect such objects early and reliably, so the machine can react in time.

Also contour detection, volume measurement, and height control are relevant applications. Especially in situations where crop structures vary greatly, LiDAR offers advantages because the technology can capture physical shape and distance in real time. This makes it easier to actively correct machine parts or better stabilize processes.

Within combine harvesters, LiDAR is used for, among other things:

• crop row detection and crop row tracking
• Obstacle detection on machine and equipment
• positioning of machine parts
• contour and volume measurement of crop
• support for semi-autonomous functions

LiDAR as a basis for more stable machine performance

For OEMs and machine builders, sensor selection ultimately comes down to system performance. A sensor must not only work well under ideal conditions but also remain stable as load, contamination, and variations increase. Harvest environments are dynamic and demanding. Vibrations, dirt load, and varying reflections mean that a technology can perform differently in practice than in a controlled test setup.

LiDAR aligns well with practical applications. By providing direct and consistent distance data, it creates a reliable starting point for machine control. This helps reduce detection errors, makes machine behavior more predictable, and limits downtime caused by incorrect interpretation. For manufacturers, this is not only relevant during development but also for the performance they need to deliver to end-users.

Furthermore, LiDAR enables machines to be prepared step-by-step for further automation. A system deployed today for detection or positioning can later also play a role in more advanced functions such as autonomous navigation, environmental modeling, and intelligent machine control.

Integrating LiDAR into an agri-project

The right LiDAR solution depends heavily on the application. Not every harvesting machine requires the same sensor, resolution, or mounting position. In some situations, a 2D LiDAR sensor is sufficient for profile measurement or detection. In other applications, 3D LiDAR is more logical, for example, when more environmental detail, volume measurement, or complex object detection is needed.

Therefore, successful integration begins not with the product, but with the intended use. Which objects need to be detected? Under what conditions must the system function? How quickly must the machine respond? And what role does LiDAR data play in the overall control or automation of the machine? By clarifying these questions early in the process, it becomes clear more quickly which sensor and configuration are best suited.

Sensor Partners supports machine builders and development teams with this consideration. Not only by supplying LiDAR sensors, but also with technical advice on measurement principles, sensor selection, and suitability for the application. This results in a solution that not only makes sense on paper, but also performs reliably in the field.

What does LiDAR deliver in harvesting systems?

The use of LiDAR helps to make critical functions within harvesters more stable and reliable. Through more accurate detection and positioning, a machine can function more consistently under conditions where other techniques are more easily disrupted. This helps to limit incorrect control, product loss, and unplanned downtime.

For machine builders, it is also important that LiDAR offers a scalable basis for further development. A system that adds value in detection or positioning today can also contribute to further automation and autonomy tomorrow.

Specifically, this results in:

• fewer detective errors during harvesting
• more stable machine control
• less unplanned downtime
• less harvest loss
• a stronger basis for further automation

Advice on LiDAR in harvest applications

Are you working on a harvester or an agricultural system where reliable detection and robust automation are important? Sensor Partners can assist you in selecting the right LiDAR sensor for your application and will help you consider the technical implementation within the system.

Whether it's obstacle detection, crop following, positioning, or preparation for further autonomy: we provide advice based on the application, circumstances, and desired performance.