An Autonomous Dual-Fan Pollination Device: Mathematical Modeling, Trajectory Optimization, and Distribution Analysis for Enhanced Crop Yield

The decline of natural pollinators, exacerbated by climate change and pesticide use, poses a critical threat to global food security. Innovative solutions are essential to mitigate these challenges and ensure sustainable agricultural practices. This study addresses the urgent need for effective, autonomous pollination systems to enhance crop productivity and resilience. This research introduces a novel artificial pollination device designed for autonomous pollen collection and dispersal. It features a custom 3D-printed funnel, a suction fan, precise artificial pollination brushes, and a blowing fan to ensure robust performance in various agricultural settings. The primary objective is to evaluate the device’s accuracy and efficiency under both controlled and environmental conditions. The device was tested in a model testbed using turmeric and mustard seeds as pollen simulants. Initial tests achieved a 61% success rate, which improved to 79% after a hardware upgrade. Further analysis using the Tracker: Video Analysis and Modeling Tool, Python, and AI tools revealed a cone-shaped pollen distribution range influenced by environmental factors. Optimized dispersion angles led to a pollen dispersal rate of 93-100%. Under typical Los Angeles weather conditions, the device’s adjusted success rate was 87.6%. This innovative artificial pollination system marks a significant leap forward in agricultural technology. It offers a scalable solution for crop pollination and urban farming, enhancing productivity in controlled environments. Future research will focus on integrating advanced sensors and AI algorithms to optimize the device’s positioning and movement in dynamic field conditions, further improving accuracy and efficiency.