Intelligent sprinkler irrigation system based on k-means algorithm

At present, the layout of sprinkler position in the irrigation system mainly depends on manual design. When the irrigation scale is large, the manual layout is difficult to realize, and the unreasonable layout is easy to cause problems such as sprinkler irrigation equipment and water resources waste. In this paper, the k-means algorithm in machine learning is applied to agricultural irrigation, and the concept of intelligent sprinkler irrigation layout design is proposed. Firstly, the sprinkler irrigation area is simulated and stored in the computer based on the inner and outer point discrimination algorithm of polygon, and then the irrigation area is clustered and divided based on the k-means algorithm to obtain each cluster. The central point of each cluster is the best location for sprinkler installation. In the comparative experiment of irrigation areas with different shapes, it is concluded that the irrigation coverage and irrigation uniformity coefficient have a good performance. Compared with manual design, the intelligent irrigation layout proposed in this paper is more intelligent and reasonable.