Agricultural Pollination Innovation: A Theoretical Analysis of the Potential Use of Robotic Bees for Hass Avocado Cultivation in Colombia

The global decline of natural pollinators poses a critical risk to agricultural productivity, particularly for crops such as Hass avocado that depend heavily on cross-pollination. This study is motivated by the need to develop alternative and scalable pollination methods capable of sustaining yields amid declining bee populations. Although recent progress in agricultural robotics has introduced drone-based pollination, there remains a lack of theoretical and quantitative analysis on how coordination strategies affect efficiency and resource use in multi-drone systems. To address this gap, this paper presents a theoretical framework and simulation-based evaluation of two coordination strategies for pollinator drones: a sweep approach, where drones systematically divide and cover orchard sectors, and a greedy approach, where each drone targets the nearest receptive flower. A stochastic orchard model with variable floral densities and temporal receptivity windows was implemented in MATLAB to evaluate both strategies under controlled factorial conditions. The simulation design provides a reproducible methodological baseline for assessing coordination dynamics under varying environmental and operational constraints. Results show that the sweep strategy achieves higher spatial coverage and balanced workload distribution, whereas the greedy strategy minimizes energy consumption but introduces task imbalance and partial coverage gaps. The comparison reveals a measurable trade-off between robustness and operational efficiency, establishing a quantitative benchmark for future studies on swarm coordination. These findings inform the design of hybrid algorithms, prototype validation, and AI-driven coordination, contributing to sustainable digital agriculture and the development of autonomous pollination systems for tropical crops such as Hass avocado.