Ant Colony Optimization for UAV-based Intelligent Pesticide Irrigation System

The application of unmanned aerial vehicle (UAV) to achieve precision irrigation in agriculture is a hot research topic in the industry. However, much spay and much leakage of pesticide are tricky for the current UAV-based irrigation methods to deal with. In this paper, we propose a new UAV-based irrigation system for precision agriculture. First, considering that different areas in the same farmland may have different pesticide shortage, a map preprocess strategy is introduced to divide the entire farmland into pieces. Second, we establish a UAV precision irrigation model and put forward an adaptive and fast dynamic ant colony optimization (AFD-ACO) algorithm to minimize the longest flight path with the lowest energy consumption and pesticide residues. In order to promote the efficiency and the optimization effect, we utilize the scent pervasion rule to make the global map preprocessed and the neighborhood adaptive search policy to accomplish planning work. Finally, comparing with other two ACO-based algorithms, the proposed algorithm is proved to be effective for the research problem, especially when the more pieces the farmland is divided, the better our solution performs.