A collaborative robotic fleet for yield mapping and manual fruit harvesting assistance

The increasing demand for agricultural products and rising production costs have intensified labour shortages in the agricultural sector. Manual harvesting remains essential for products with specific designations, such as wine grapes, where automated solutions cannot match human operators’ dexterity, speed, and care. Minimizing transportation time is also crucial for preserving produce quality and optimizing efficiency. This study aims to optimize harvesting efficiency and vineyard management through the design and implementation of a mobile robotic platform. The platform combines operator dexterity with robotic assistance, continuously tracking operators as they deposit harvested grapes into a harvesting box carried by a robot while gathering data for yield map development. Adaptable to various manual fruit-picking processes, the platform can be integrated into a collaborative harvesting assistance fleet. Field experiments conducted at the Bodegas Terras Gauda (UTM coordinates: 41.95, −8.80, O Rosal, Pontevedra, Spain) vineyard, indicated that operators using robotic assistance reduced their average harvesting time per box by 6 min, increased their total harvested yield by 72.50 kg after two hours (up to 50% more), and reduced manual labour costs by 22.50%. A yield map was developed with high-accuracy GNSS data and an industrial scale mounted on the robot. The map geolocates the weights collected with a maximum variability error of 0.11 kg and successfully expresses grapevine density variability within the same vineyard row. The system preserves produce quality during transportation and significantly eliminates physical strain among operators. These results demonstrate the potential of the robotic platform to improve the efficiency of manual harvesting while maintaining high-quality outcomes.