Path Planning and Target Recognition of Intelligent Greenhouse Agricultural Multifunctional Robot

Challenges are being faced by traditional agriculture due to the acceleration of population growth and urbanization. Precise environmental control is achieved by integrating advanced technologies and the Internet of Things in intelligent greenhouse agriculture, which also leads to increased yield and quality, as well as cost reduction. Various agricultural tasks are autonomously navigated to be completed by multi-functional robots, thus improving the automation level of greenhouse agriculture. A path planning method combining the A* algorithm and genetic algorithm, along with deep learning target recognition technology, is proposed in this paper. Improved dynamic obstacle avoidance, smoother paths, and enhanced computational efficiency are shown by the experimental results, with high target recognition accuracy being achieved. A technical foundation is provided by this for the sustainable development of intelligent greenhouse agriculture, and the collaboration between greenhouse robots and environmental control systems is helped to be optimized.