The study introduced a robotic pollination system for apples, which are not self-pollinating and require precise delivery of pollen to the stigmatic surfaces of the flowers. The system comprised a machine vision component to identify and locate target flower clusters, a robotic manipulator and motion planning system for collision-free navigation, and an electrostatic sprayer-based end-effector system to spray positively charged pollen suspension to the target flower clusters.
The machine vision system achieved a mean average precision of 0.89 in identifying and segmenting apple flower clusters. Field trials in 'Honeycrisp' and 'Fuji' apple orchards showed the robotic pollination system could pollinate flower clusters at an average spray cycle time of 6.5 seconds.
The robotic pollination approach achieved fruit set comparable to natural pollination, with the 2 gm/l pollen concentration performing better than the 1 gm/l concentration. Fruit quality assessment showed the robotically pollinated fruits were generally comparable to naturally pollinated fruits in terms of color, weight, diameter, firmness, soluble solids, and starch content. However, the results varied between apple cultivars and pollen concentrations.
The study demonstrates the potential for a robotic artificial pollination system to be an efficient and sustainable method for commercial apple production. Further research is needed to refine the system and assess its suitability across diverse orchard environments and apple cultivars.
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by Uddhav Bhatt... at arxiv.org 10-01-2024
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