Enhancing Greenhouse Pollination with CNN-Based Micro-UAV for Real-Time Flower Detection

This paper presents the application of a micro unmanned aerial vehicle (UAV) that acts as a pollination agent in a controlled environment simulating greenhouse conditions. The micro-UAV system was integrated with a convolutional neural network (CNN) for autonomous flower detection and navigation. The ResNet-18 CNN architecture is was utilized onboard to perform real-time binary classification to accurately distinguish flowers from non-flower objects. The fusion of this deep-learning-based detection with precise micro-UAV navigation enables efficient identification and approaches to target flowers within optimal operational distances. Experimental evaluations revealed that the micro-UAV’s onboard camera combined with CNN processing outperformed standard webcams in terms of detection speed and accuracy, demonstrating the benefits of specialized hardware. Within the experiment, micro-UAV was pre-programmed to follow a ‘cross’-shaped flight pattern. Experimental result shows that the proposed system successfully detecting multiple flowers autonomously between distance of 30.5 cm to 91.5 cm within 149.1 seconds. Overall, this study validated the integration of neural network capabilities with micro-UAV navigation. These findings are crucial for drawing attention to the potential of neural network-enabled micro-UAVs as effective pollinators in enclosed agricultural environments and addressing the challenges faced by natural pollinators in greenhouses.