Digital Collection and Display System of Agricultural Tourism Resources Based on Drones and Computer Vision

This study designed a digital collection and display system of agricultural tourism resources based on drones and computer vision, and proposed an innovative algorithm model. The system combines drone flight path planning and data collection, and uses computer vision technology for image preprocessing, target detection and feature extraction to achieve automatic collection and accurate identification of agricultural resources. To improve the recognition accuracy, this paper uses CNN to classify agricultural resources, and further optimizes the accuracy of target recognition by combining the integrated learning framework of deep learning. In addition, this study introduces reinforcement learning and genetic algorithms to optimize the flight path and target recognition algorithm to improve the collection efficiency and system performance. The experimental results show that the algorithm model has an accuracy rate of 92.5% in target recognition of agricultural resources, and achieves about 20% time saving in the optimization of the collection path. Compared with the traditional method, the overall processing speed of the system is increased by 15%. The simulation experiment also demonstrates the stability and adaptability of the system in different environments (such as different terrains and climatic conditions).