What can the agricultural market learn from lidar applications in other sectors?

Lidar is being increasingly used in autonomous systems and robots. The technology makes it possible to perceive the environment in 3D and accurately detect objects. This is valuable for machines that need to navigate, recognize obstacles, or map their surroundings.

There are also opportunities for this in agriculture. By looking at lidar applications in other sectors, new ideas are emerging for autonomous farm machinery, field robots, and precision agriculture.

Challenges in the agricultural market and together

Sensor integration in agricultural machinery places high demands on technology. Dust and dirt, vibrations, rain, sun, and temperature fluctuations are commonplace. At the same time, machines operate in dynamic environments involving crops, people, animals, and other vehicles.

Many autonomous systems outside the agricultural market face similar conditions. Three applications show what can be learned from this.

1. Autonomous navigation between obstacles

Trombia develops autonomous electric street sweepers that navigate independently using lidar and detect obstacles. Ouster's digital lidar continuously creates a 3D image of the environment, allowing the machine to recognize objects and people and navigate safely around them.

Because lidar is an active sensor with its own light source, the technology also works in the dark without additional lighting. Lidar also offers advantages in situations with dust or varying lighting conditions.

In agriculture, the same principle is applicable, for example, to feed pushing robots that drive independently along fences and feeding areas or field robots that navigate autonomously between crop rows.

2. Autonomous driving in off-road environments

Forterra develops technology that enables vehicles to drive autonomously in complex off-road environments, without lanes and with limited visibility or GPS.

Lidar continuously provides these vehicles with an accurate 3D image of their surroundings. Distances and obstacles are measured, allowing the vehicle to navigate even without fixed reference points.

The similarity with agriculture is clear: there, too, machines drive off-road and have to deal with changing weather, pollution, and irregular terrain. Consider, for example, self-driving tractors and unmanned field robots.

3. 3D mapping of terrain and vegetation

Deep Forestry uses autonomous drones with lidar to scan forests and create detailed 3D maps. The lidar measures distances to trees, terrain, and other objects and converts these measurements into a point cloud.

The same principle offers possibilities for precision agriculture. Consider 3D mapping of fields, volume measurements of crops or harvested products, and analysis of terrain and vegetation.

From lidar sensor to working application

A suitable sensor is only one part of a reliable lidar solution. When integrating it into an agricultural machine, the following factors, among others, play a role: mounting position, protection against mud and water, vibrations, and processing of the large amount of sensor data an important role.

For digital lidar sensors, Sentech collaborates with Ouster. Ouster's robust lidars form the basis, while Sentech provides support with the selection and integration of the technology into the application.

By translating knowledge from other sectors to the specific conditions of agricultural machinery, not only is an interesting sensor technology created, but also a solution that works in practice.