Application

Autonomous leaf cutting robot for high wire cultivation

Technology

Artificial intelligence is the USP of our modular robot platform

Products

The CropTeq platform will do more than just cut leaves

Our technology and automation knowledge

With our knowledge and technology, we have developed an autonomous leaf-cutting robot for cucumber plants in high-wire cultivation. With some smart adjustments for the crop and a small adjustment of the AI algorithm, the robot is also suitable for tomato cultivation. 

Autonomous harvesting of cucumbers is also part of our roadmap, as is automation for other crops or flowers. The end effector (patent) for leaf cutting is intrinsically tolerant to positioning and cutting, making it robust for autonomous positioning. In the future, other end-effectors will also be placed on the interface. 

Think, for example, besides harvesting also precision spraying, insect detection and removal. There is a huge perspective for more with the CropTeq. In addition, we have chosen to provide the system with an option for data collection, with a perspective on, for example, harvest forecasts. Data is a much discussed topic. We measure and collect. Based on this, the grower can give his own interpretation.

Our Artificial Intelligence

A robot that has to cut leaves autonomously and therefore has to adapt continuously needs AI. With a vision system, a smart neural network and a correctly chosen camera, it is possible to recognize an object and its position. Using additional techniques and a plant model, we have developed a movement and end-effector strategy.

With enough images you can train AI algorithm so that the system recognizes the right things. Training is a new word for programming. However, you do not have to be a software specialist for this. Refining the algorithms, recognizing the plant, does require knowledge and insight. Once you have them, it is something that the grower could also improve on.

Artificial intelligence therefore makes the recognition software itself, and not a human being. AI software is therefore never 100% reliable, so you cannot cut 100% of the leaf or harvest a fruit with it. For example 95%, but that can sink in due to different light incidence, etc. Despite this limitation, it is the AI ​​that makes the automation in the greenhouse possible.

Data

A robot generates a significant amount of data. All this data is stored in a Data Lake through our IoT platform. It can be enriched as desired and securely transmitted to various third parties. There are three types of data: machine data, application data, and training data. Machine data is analyzed to monitor the system, aiming for continuous performance improvement. For example, in energy management, calculating energy consumption may differ in practice, requiring load strategy and necessary capacity considerations.

Application data, of interest to growers, includes continuous measurements of temperature, light, humidity, and CO2. Analysis of this data allows conclusions to be drawn, adapting the cultivation process accordingly. While we don't default to measuring this raw data, it can be added. The raw data remains the property of the grower, who may seek assistance from a specialist to analyze and increase its value.

The third type is training data. Our robots log all visual material where points of interest aren't recognized with sufficient certainty by the vision system. This material is used for reinforcement learning of the neural network, resulting in a rapid learning curve for changing conditions as robots learn from each other's experiences.

Robot path planning.

Anyone can program a robot to move from A to B. The real challenge is enabling a robot to accurately follow a "moving stem" without compromising performance.

CropTeq combines real-time vision, visual servoing, and motion planning to track cutting locations while inevitably making contact with the plant. The result is a system that balances speed, precision, and crop safety.

In greenhouse robotics, success is measured by achieving maximum productivity while minimizing crop damage.

End effector: accurate, fast and economical. 

Accuracy is generally not a problem for a robot. In the greenhouse, however, the camera and vision system determine the inaccuracy. The robustness of the cutting system is therefore decisive. We have opted for a patented gripper that also stresses the plant as little as possible.

A robot can be very fast. On the one hand, the safety requirements limit the speed of movement of the cobot arms, on the other hand careful handling of the plant also limits the speed. The bottom line is that mainly the movement from leaf to leaf and the cutting cycle time Is critical. The vision system, the image processing are not in the critical path of the movement planning.

A robot is not automatically economically viable. Full autonomy requires extensive sensing technology and significant computing power. And one should not overlook the development costs that must ultimately be recovered. Despite this, the cost per leaf remains competitive, provided the system achieves the required speed and uptime.

System speed is essential and will continue to improve as the balance between speed and accuracy is further optimized. Software plays a dominant role in this process, which means that a significant portion of the operational costs is dedicated to the continuous development, maintenance, and regular updating of the software.