Research on the Optimization of Machine Learning - Based Agricultural Irrigation Equipment

Abstract

This article proposes a machine learning-based optimization method for agricultural irrigation equipment. Firstly, data and discretization processing are applied to irregular farmland to extract edge data and internal data points. Secondly, the k-means clustering algorithm is employed to optimize the positions of sprinklers, minimizing the distance between sprinklers within the same group and maximizing the distance between different groups, achieving intelligent allocation of sprinkler positions. Finally, a specialized agricultural irrigation equipment optimization software is designed and developed, capable of importing farmland data, setting random seeds and sprinkler quantities, applying the k-means clustering algorithm for optimization, and displaying the optimized results. This method utilizes machine learning algorithms to achieve intelligent optimization of agricultural irrigation equipment, enhancing water resource efficiency, reducing irrigation costs, and providing support for sustainable agricultural development.