Integration of machine learning models with real-time global positioning data to automate the wild blueberry harvester

Abstract

Efficient mechanical harvesting of wild blueberries across uneven topographies calls for precise header height adjustments to optimize fruit picking. Conventionally, an operator requires manual adjustment of the harvester header to accommodate the spatial variations in plant height, fruit zone, and field terrain. This can result in inadequate header positioning, which leads to berry losses and increased operator stress. This study aimed to investigate the integration of machine learning techniques with real-time geo-location data to develop an innovative system to automate harvesting operations. A supervised machine learning Random Forest (RF) model was trained based on pre-defined header setting data and integrated with the harvester’s controller to predict and position the header height using real-time geo-location data from the Starfire (SF) 6000 Global Positioning System (GPS) receiver. During harvesting, the system’s performance was evaluated at tractor ground speeds (0.31, 0.45, and 0.58 ms⁻¹) and segment lengths (5, 10, and 15 m). Results indicated that segment size minimally affected the system’s ability to adjust header height. However, at the lowest segment length, 5 m, the coefficient of determination was 97.24, 98.12, and 82.71% for the 0.31, 0.45, and 0.58 ms⁻¹, respectively. This study provided convincing results for automating the harvester header based on pre-defined settings, marking a significant step toward complete automation of the wild blueberry harvester. Automation of wild blueberry harvesting can help to increase picking efficiency and enhance profit margins for growers to justify the ever-increasing cost of production.