A vision-based robotic system for precision pollination of apples

Abstract

Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to factors such as climate change, habitat loss, and pesticide use. This paper presents an integrated robotic system for precision pollination in apples. The system consisted of a machine vision system to identify target flower clusters and estimate their positions and orientations, and a manipulator motion planning and actuation system to guide the sprayer to apply charged pollen suspension to the target flower clusters. The system was tested in the lab, followed by field evaluation in Honeycrisp and Fuji orchards. In the Honeycrisp variety, the robotic pollination system achieved a fruit set of 34.8% of sprayed flowers with 87.5% of flower clusters having at least one fruit when a 2 gm/l pollen suspension was used. In comparison, the natural pollination technique achieved a fruit set of 43.1% with 94.9% of clusters with at least one fruit. In Fuji apples, the robotic system with same pollen concentration achieved lower pollination success, with 7.2% of sprayed flowers setting fruit and 20.6% of clusters having at least one fruit, compared to 33.1% and 80.6%, respectively, with natural pollination. Fruit quality analysis showed that robotically pollinated fruits were comparable to naturally pollinated fruits in terms of color, weight, diameter, firmness, soluble solids, and starch content. Additionally, the system cycle time was 6.5 s per cluster. The results showed a promise for robotic pollination in apple orchards. However, further research and development is needed to improve the system and assess its suitability across diverse orchard environments and apple cultivars.