Calculate first, then invest in robotic harvesting
An apple harvesting robot may be technically promising, but in the orchard the business case also matters. Economic ROI Validation is now available for apple harvesting robots and makes this calculation well-founded and comparable. What does the system cost per kilo of product, what changes in labour and planning, and when will the investment pay back within a specific business situation? The method helps growers, developers and other involved parties see which assumptions carry the most weight. This makes it clearer whether investment makes sense, further development is still needed or more practical data is required first.
Broad knowledge question
The technical promise needs an economic test
Robotic harvesting is becoming more relevant because of questions around labour availability, costs and efficiency. At the same time, an application can only be assessed seriously when it is clear when it becomes competitive with manual labour. The broader knowledge question is how the economic feasibility of harvesting robots can be calculated reliably in different cultivation systems. For apple harvesting robots, this means looking at technology, costs, benefits, payback time and the business situation together.
Approach
A calculation model shows which assumptions make the difference
The method calculates costs and returns and compares robotic harvesting with manual labour in several scenarios. It uses an extensive Excel-based calculation model, in which fixed, variable and indirect costs are linked to returns and efficiency. Conversations with growers, technology developers, researchers and suppliers help bring the input values closer to practice. Future scenarios are then calculated, including inflation, technological progress and developments in the labour market.
Goal
Better choices are made when parties look at the same calculation
The goal is to provide a method that assesses the economic value of robotic harvesting technology step by step and in a repeatable way. For growers, this offers guidance on whether an apple harvesting robot fits their labour situation, planning and investment capacity. For developers, it shows under which conditions their solution can become more financially attractive. This makes conversations about further development, application and financing more concrete.
Result and reflection
The outcome shows what profitable robotic harvesting depends on
Economic ROI Validation shows under which circumstances robotic harvesting systems can become competitive with manual labour. The tool provides insight into costs per kilo of product, payback time, profitability and dependency on different scenarios. In the short term, the method can be broadened to other crops that are still largely harvested manually, such as soft fruit, tomatoes and cucumbers. In the longer term, the approach can also help compare outcomes between different harvesting robots more effectively, provided that assumptions and calculations are shared.
Successful outcomes:
The method makes costs, benefits, payback time and profitability discussable in concrete figures.
Scenarios show which assumptions have the strongest effect on the outcome.
The model works as a conversation tool between growers, developers and other involved parties.
Lessons learned:
The quality of the outcome depends on realistic input from practice.
The economic test must always be completed for the crop and business situation for which a decision is needed.
In the longer term, comparing robots requires shared assumptions and repeatable calculations.