Tasks

For this task, the robots are navigating autonomously through a real maize field. Turning must follow adjacent rows for track 1 to 5. From exiting track 5 the robot must follow a given particular turning pattern. This task is all about accuracy, smoothness, and speed of the navigation operation between the rows. Within three minutes the robot navigates between the rows. The aim is to cover as much travelled distance as possible. You find an example field and driving pattern in Figure 1.1. The first 3 tracks are without intra-row gaps to make it easy for robots to start. The rest of the field – track 4 to 11 – there are intra-row gaps even sometimes on both sides. In the last part – after track 5 – the robot has to follow a particular given turning and row pattern. The pattern may look as: S – 1L – 1R – 3L – 2L – 2R – F.

Random stones and pebbles are placed along the path. Therefore, machine ground clearance is required. In order to make it easier for sensors there will be no gaps at the row entries and exits. The ends or beginnings of the rows may not be in the same line. The headland will be perhaps indicated by a fence or ditch or similar.

Robots must autonomously navigate through the maize field while detecting signs of unhealthy plants. Some maize plants will be marked to represent diseased crops, for example with yellow or brown leaf markers attached to the plant or a nearby stick.

The robot must identify these unhealthy plants while driving through the field. A detection is only considered valid when the robot recognizes the plant within a detection range of 40 cm. To make this visible to both the judges and the audience, each robot is equipped with clearly visible markers that indicate the start and end of this detection area.

Whenever a diseased plant is detected, the robot must provide a clear external signal. This may be done using lights, sound, spoken messages, or other visible indicators. Healthy plants should not trigger any signal. In addition to the visible signal, teams can optionally submit a digital log file containing the detected plant locations.

The scoring system rewards correct detections and penalizes false positives or missed diseased plants. Additional penalties apply if maize plants are damaged during navigation, and a time bonus can be earned when the task is completed faster than the maximum time.

In this task, robots must detect and classify insects that appear in the maize field. These insects are represented by models or images mounted on green sticks placed along the rows. The insects represent different ecological roles in agriculture: bees (beneficial insects), beetles or aphids (pests), and butterflies (neutral species).

While navigating through the rows, the robot must identify each insect and classify it correctly. Each insect station must be detected within a detection range of 40 cm relative to the robot. As in previous tasks, robots are equipped with visible markers that indicate the valid detection zone.

Once an insect is detected, the robot must visibly and audibly communicate its classification. For example, a green signal may indicate a beneficial insect such as a bee, a red signal may indicate a pest, and a yellow signal may indicate a neutral insect such as a butterfly.

The scoring system rewards correct classifications while penalizing incorrect classifications, missed stations, or damage to maize plants.

In this task, robots must detect and treat specific spots in a test field that represent areas requiring soil treatment. These spots are marked using small coloured discs placed on the ground. Each disc represents a location where an agricultural action must be performed.

Robots must autonomously navigate within a designated test strip and locate these markers. When a spot is detected, the robot must perform a visible treatment action. Examples include spraying a small jet of water, dropping a biodegradable pellet, or performing a mechanical soil treatment such as hoeing.

The treatment must remain visible after the robot has passed the spot so that judges can verify the action. Each treatment is evaluated based on how accurately it is performed relative to the marker location.

The scoring system rewards accurate treatments close to the marked spot and penalizes missed spots, incorrect actions, or damage to the markers. Additional bonus points may be awarded when the robot performs an action that directly engages the soil.

In this task, teams demonstrate a self-designed agricultural application using their robot. Unlike the other tasks, the focus is on creativity and real-world relevance rather than a predefined problem.

For the 2026 event, teams are encouraged to focus on sustainability. This may include solutions that reduce water use, improve energy efficiency, support biodiversity, or reduce the use of chemicals in agriculture.

Teams have up to five minutes to demonstrate their robot and briefly explain their concept to the audience and judges. The performance must be clearly visible and safe.

Scoring is based on audience and jury voting. Audience members and judges receive voting tokens that they can assign to the teams they believe present the most innovative idea, the most advanced technical solution, or the best overall execution.