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.

在不平坦的地形上高效机械收割野生蓝莓需要精确的割台高度调整，以优化水果采摘。传统上，操作员需要手动调整收割机割台，以适应植物高度、水果区和田间地形的空间变化。这可能导致割台定位不足，从而导致浆果损失和操作员压力增加。本研究旨在调查机器学习技术与实时地理位置数据的集成，以开发一种创新系统来自动化收获操作。监督式机器学习随机森林 （RF） 模型基于预定义的割台设置数据进行训练，并与收割机的控制器集成，以使用来自 Starfire （SF） 6000 全球定位系统 （GPS） 接收器的实时地理位置数据预测和定位割台高度。在收割过程中，在拖拉机的地面速度（0.31、0.45 和 0.58 ^ms-1）和节片长度（5、10 和 15 m）下评估了系统的性能。结果表明，区段大小对系统调整标题高度的能力影响最小。然而，在最低节段长度 5 m 处，0.31、0.45 和 0.58 ^ms-1 的决定系数分别为 97.24、98.12 和 82.71%。这项研究为基于预定义设置的收割机割台自动化提供了令人信服的结果，标志着野生蓝莓收割机朝着完全自动化迈出了重要一步。野生蓝莓收获的自动化有助于提高采摘效率，提高种植者的利润率，以证明不断增长的生产成本是合理的。