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Highlights

Three-season field study evaluated conventional and mechanized apple harvest operations.

ABSTRACT. Meeting tight harvest schedules amid labor shortages is a major challenge for apple orchard managers. Although mechanical harvest platforms can improve efficiency, their adoption remains limited—partly due to uncertainty about their economic viability. Modeling the costs and productivity of these platforms can help managers decide whether to invest, which model and brand to purchase, and how to deploy them effectively to meet harvest goals. This study draws on in-field data collected over three seasons and three apple varieties, each requiring a distinct harvest method (e.g., one-pick, spot-pick). We evaluate various labor and machinery configurations in terms of bin filling time, person hours, and fruit quality. While bin filling time is not consistently affected by setup across all methods, person hours—a key indicator of labor efficiency—are significantly influenced. These findings highlight the importance of selecting machine–labor combinations suited to specific harvest methods and underscore the value of work and motion studies in designing cost-effective harvest strategies. Field data were used to develop a cost and productivity model that integrates labor, machinery, and harvest targets to support informed decision-making in orchard operations. Model simulations allowed evaluation of cost and productivity under varying labor and machinery availability. Within the context of a specific orchard and labor availability, results showed that without a platform, approximately 25% of the yield remained unpicked. Introducing a single single-deck machine staffed with five workers reduced unpicked yield to 9%, with an 11% increase in total cost. Using two platforms enabled full harvest completion within the designated timeframe, raising total cost by 16%. The model is implemented in an online calculator designed to assist orchard managers in assessing the cost-effectiveness of conventional and mechanized harvest approaches and in making informed investment decisions. With a general and adaptable structure, the model can be extended to other crops (e.g., citrus, pomegranate), harvest-assist technologies, and diverse operational settings.