Public summary

Technical and economic validation of apple-picking robot

Munckhof Fruit Tech Innovators

February 27, 2025

More efficient harvesting with autonomous technology

The use of autonomous robots can help address labor shortages in fruit growing and make the harvesting process more efficient. Munckhof Fruit Tech Innovators tested an apple-picking robot to validate its performance, efficiency, and stability. This study lays the groundwork for further optimization of autonomous harvesting in the sector.

Innovation package, use case, and type of trial

    Status: evaluation report

    Technical functionality

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    Brede kennisvraag

    How does an autonomous apple-picking robot perform in practice?

    This study focuses on evaluating the performance of an autonomous apple-picking robot and identifying areas for improvement. In 2023, the emphasis was on establishing a baseline for future comparisons, with attention to accuracy, stability, and economic feasibility.

    Approach

    Testing picking performance and stability

    To assess the robot's operation, two types of tests were conducted:

    1. Single-cycle tests, analyzing the picking performance per apple.

    2. Durability tests, evaluating the robot’s stability and reliability over extended periods.

    During the test period, some technical adjustments were made to compensate for limitations in the testing method.

    Goal

    Optimization of autonomous harvesting

    The goal of these validation tests is to evaluate the efficiency, picking performance, and stability of the apple-picking robot. Wageningen University & Research conducted independent validations to objectively assess the results. The insights from this study form the basis for further technological improvements.

    Results and reflection

    Valuable insights and key considerations

    The initial tests provided a clear picture of the performance and stability of the apple-picking robot, but there are still areas for improvement.

    Successes:

    • All planned measurements were carried out, providing valuable insights.

    • The robot’s stability was assessed over extended periods.

    Lessons learned:

    • Technical limitations affected the completeness of the data, requiring further optimization.

    • The accuracy of the counting system needs improvement for better performance analysis.

    • The picking costs per kilogram could not yet be fully calculated, necessitating further economic evaluation.

    Leading Partners involved