A CNN-Based Micro-UAV System for Real-Time Flower Detection and Target Approach

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 custom Sequential CNN architecture was used on board to perform real-time binary classification, accurately distinguishing 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, the micro-UAV was pre-programmed to follow a ‘cross’-shaped flight pattern. Experimental results show that the proposed system successfully detects multiple flowers autonomously between distances of 30.5 cm and 91.5 cm within 149.1 s. Overall, this study validated the integration of neural network capabilities with micro-UAV navigation. These findings are crucial for highlighting the potential of neural network-enabled micro-UAVs as effective pollinators in enclosed agricultural environments and for addressing the challenges faced by natural pollinators in greenhouses.