Oxbo’s AutoHarvest brings machine learning to blueberries

Oxbo has added machine-learning automation to its berry harvesters with AutoHarvest, a system that reads field conditions in real time and adjusts machine functions on the fly rather than leaving those calls to the operator. The technology is available now on 2027 Oxbo blueberry harvesters, the company announced today. AutoHarvest relies on integrated cameras and algorithms to make ongoing adjustments as the machine moves through the field. Operators or field managers set harvest goals through two input sliders, and the system then works in the background to fine-tune performance. It manages ground speed, head speed, head pinch, and belt and fan speeds continuously, targeting consistent results across changing conditions. The design addresses one of the harder parts of berry harvesting: keeping output steady when field conditions shift within and between passes. Rather than requiring the driver to make manual changes, AutoHarvest reads the crop and adapts the machine to match the defined goals. Oxbo says the system was developed to work across a range of harvesting conditions and production targets. Whether a grower is harvesting for fresh-market quality or trying to maximize processed fruit recovery, AutoHarvest is intended to keep machine functions aligned with the desired outcome throughout the season. “AutoHarvest simplifies one of the most challenging parts of berry harvesting by helping operators achieve consistent results across changing field conditions,” said Kathryn Vanweerdhuizen, Director of Sales & Marketing for Oxbo Fruit. Vanweerdhuizen said the technology is designed to help operators recover all the ripe fruit on each pass, and that it lowers the skill barrier for dialing in the machine. “AutoHarvest allows operators of any skill level to expertly set and fine-tune your harvester for the variety, conditions, and fruit program goals,” she said. The skill-leveling claim carries weight for growers managing seasonal labor, where experienced harvester operators can be difficult to find and retain. By moving the fine-tuning into an automated layer, the system reduces reliance on operator judgment to hit consistent quality and recovery targets. Oxbo has facilities in Lynden, Washington; Marshfield, Wisconsin; Madera, California; and Byron, New York; and two locations in Europe.