Public summary

Apple-picking robot evaluation

Munckhof Fruit Tech Innovators

February 27, 2025

Robotics as a solution for labor shortages in fruit growing

The availability of labor in agriculture and horticulture is decreasing, while the demand for more efficient and sustainable cultivation methods is increasing. Munckhof Fruit Tech Innovators is testing an apple-picking robot to determine the extent to which this technology can improve labor efficiency and economic feasibility of autonomous harvesting.

Innovatiepakket, use case en type test

    Status: evaluation report

    Technical functionality

    Menu card category

    Broad research question

    How can technology support labor and efficiency in fruit growing?

    Autonomous robots can help address labor shortages, but their technical and economic performance must be carefully evaluated. This study focuses on measuring the picking efficiency and costs of the apple-picking robot, aiming to identify areas for improvement.

    Approach

    Step-by-step evaluation of technology and economics

    The testing and validation phase consisted of four components:

    1. Preliminary research: Testing the experimental protocol and initial evaluation of the robot in the orchard.

    2. Testing and validation during the harvest period: Harvest experiments conducted over multiple days and weeks.

    3. Economic validation: Inventory of all costs and calculation of economic KPIs.

    4. Data analysis and reporting: Processing measurement results and recommendations for further optimization.

    Goal

    Determining readiness for the next project phase

    The project evaluates the current performance of the apple-picking robot based on established Key Performance Indicators (KPIs). The results from the 2024 season are used to determine whether the robot is ready for further development and scaling.

    Results and reflection

    Technical progress and economic challenges

    The technical KPIs for this season have been met, but optimizations are still needed to improve economic feasibility.

    Successes:

    • The robot achieved all planned technical KPIs.

    • The collected data provides insight into performance and potential improvements.

    Lessons learned:

    • The accuracy of the automatic counting system needs improvement to optimize integration into the digital infrastructure.

    • The cost per kilogram of harvested apples currently exceeds the target of €2.00.

    • Calculations indicate that higher picking speeds and additional robot arms could significantly reduce costs.

    Leading Partners involved