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This Robot Doesn’t Just Pick Strawberries—It Moves Obstacles Out of the Way Using New Technology

Strawberry harvesting may look simple, but for a robot, picking a ripe strawberry can be surprisingly difficult. The fruits are delicate, often grow in tight clusters, and can be hidden behind leaves, stems, or other strawberries. A robot must identify the right fruit, reach it without causing damage, remove surrounding obstacles, and place the strawberry carefully into a container.

To address these challenges, Ya Xiong and his research team developed an autonomous strawberry-harvesting robot designed to continuously pick fruit inside polytunnels. The system combines computer vision, robotic arms, intelligent path planning, and an improved gripper to deal with the complex conditions found on real farms.

Why Strawberry Harvesting Is So Difficult

Strawberries are grown extensively around the world, both in open fields and controlled environments such as greenhouses and polytunnels. However, harvesting remains highly dependent on human workers.

One major reason is the delicate nature of ripe strawberries. Unlike some harder fruits, strawberries can easily be damaged during handling. A harvesting robot therefore needs to use controlled and gentle movements.

Another problem is the way strawberries grow. Several fruits can develop close together, with leaves and stems surrounding them. A robot that can easily pick an isolated strawberry may struggle when the target fruit is partly hidden or located inside a dense cluster.

Labor costs and availability have also increased interest in agricultural robots. Manual harvesting can represent a significant portion of strawberry production costs, making automation an attractive area of research.

The Robot's Biggest Innovation: Moving Obstacles

One of the most interesting features of Xiong's system is its active obstacle-separation algorithm.

Instead of simply trying to find a clear path toward a strawberry, the robot can use its gripper to push surrounding obstacles out of the way.

These obstacles may include leaves, nearby strawberries, and other parts of the plant.

The process works in two main stages. Before the robot reaches the fruit, the gripper can push aside obstacles near the lower part of the target. As the gripper moves toward the strawberry, it can also separate obstacles located above the fruit.

The robot calculates suitable pushing directions using information from a 3D point cloud captured by its vision system. The point cloud is simplified into smaller blocks, allowing the system to estimate where surrounding obstacles are located and determine how they should be moved.

This approach is important because agricultural environments are rarely perfectly organized. Plants can grow differently from one another, meaning that a robot cannot always depend on a fixed movement pattern.

Smarter Vision for Changing Light

Computer vision is another major challenge for agricultural robots.

Outdoor and polytunnel environments can experience significant changes in lighting. Sunlight can become brighter or weaker, shadows can appear, and the same strawberry may look different under different conditions.

To deal with this problem, the researchers developed an improved vision system based on modeling color in relation to light intensity.

Instead of relying on one fixed color threshold, the system can adapt its thresholds according to changing lighting conditions. This makes strawberry detection more resilient when the environment changes.

Reliable vision is essential because the robot needs to identify the target fruit and understand the position of nearby obstacles before deciding how to move.

A Low-Cost Dual-Arm Design

The researchers also developed a low-cost dual-arm robotic system.

Using two robotic arms provides an important advantage: the system can work on multiple strawberries more efficiently while reducing unnecessary movement.

The team studied the order in which the arms should harvest strawberries. By selecting an appropriate harvesting sequence, the robot can improve efficiency while reducing the possibility of the two arms colliding with each other.

In one-arm operation, the system required about 6.1 seconds for the manipulation operation, including the arm's movement toward the next target.

With two arms working together, the average time decreased to approximately 4.6 seconds per strawberry.

For agricultural robots, reducing the time required for each fruit is particularly important because commercial harvesting involves potentially thousands of fruits.

Strawberries Can Go Directly Into the Market Punnet

The researchers also improved the robot's gripper.

One particularly useful modification allows the robot to harvest strawberries directly into a market punnet, the type of container used for selling fresh fruit.

This eliminates an additional repacking step. Instead of collecting strawberries somewhere else and later transferring them into retail containers, the robot can place them directly into the final container.

This could potentially reduce both handling time and the risk of additional damage caused by unnecessary movement.

How Well Did the Robot Perform?

The researchers tested the complete system on a real strawberry farm.

The results showed that the robot could successfully pick partially surrounded and isolated strawberries, although performance depended strongly on the way the fruit was positioned within the plant.

On the first attempt, the success rate ranged from 50% to 97.1% across different growth situations.

When the robot was given another attempt, the success rate increased to between 75% and 100%.

These results demonstrate that the robot was capable of dealing with many of the complicated situations encountered in real strawberry plants.

However, there was still a major limitation.

When a target strawberry was completely surrounded by obstacles, the robot struggled significantly. In this situation, the first-attempt success rate was only 5%.

According to the researchers, the failures were mainly related to limitations in the vision system and the insufficient dexterity of the gripper.

How It Compares With Earlier Harvesting Robots

Strawberry-harvesting robots have been studied for many years, but achieving reliable performance in real farming environments has remained difficult.

Earlier systems often focused on detecting the strawberry or its stem and then using cutters or other mechanisms to remove the fruit. Some systems achieved moderate success but struggled with problems such as incorrect stem detection, collisions with plant structures, or long harvesting cycles.

Other agricultural robots have faced similar challenges with crops such as apples, tomatoes, cucumbers, and sweet peppers.

The Xiong team's approach is different in an important way: the robot does not simply avoid obstacles—it can actively move them.

That capability could be useful beyond strawberries. Similar techniques might eventually be adapted for other crops where fruits are hidden behind leaves, stems, or neighboring fruits.

The Road Ahead for Robotic Farming

The research demonstrates how several technologies can work together to solve a difficult agricultural problem.

The robot combines adaptive computer vision, 3D obstacle detection, intelligent path planning, robotic manipulation, dual-arm coordination, and direct-to-punnet harvesting.

However, the technology is not yet perfect. Completely enclosed strawberries remain difficult to harvest, while improvements in vision and gripper dexterity are still needed.

Even so, the results show an important step toward practical autonomous harvesting. A robot that can operate continuously in a real polytunnel, identify strawberries under changing lighting, move obstacles, and place fruit directly into market containers brings agricultural automation closer to real-world applications.

As labor shortages and harvesting costs continue to challenge growers, technologies like this could become increasingly important. The ultimate goal is not simply to build a robot that can pick one strawberry—it is to create a reliable system capable of working continuously, efficiently, and gently across an entire crop.

Reference: Xiong Y, Ge Y, Grimstad L, From PJ. An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation. J Field Robotics. 2020; 37: 202–224. https://doi.org/10.1002/rob.21889

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