Screening of Drought-Resistant Potato Germplasm Resources Based on Intelligent Algorithm

Abstract

As a staple food resource, potato is of great significance for improving grain reserves and ensuring national food security. In order to improve potato yield and promote the process of potato becoming a staple food, a K-means algorithm optimized by particle swarm algorithm was proposed to realize the screening of dry potato germplasm resources. First, the research continues the research on particle swarm optimization, and innovatively applies K-means algorithm to optimization. The research utilizes the advantages of particle swarm optimization, such as fast convergence speed, strong search ability, and simple operation, to enable particle swarm optimization to take on the role of optimizing the initial clustering center, thereby improving the accuracy and efficiency of clustering analysis. On this basis, a PSO-K-means drought resistant potato germplasm resource screening model was constructed. This model consists of a data collection and preprocessing module, an impact indicator determination module, and a comprehensive evaluation module. Finally, the application effect of the model was verified. The results show that the AUC value of the model is up to 0.840, and the screening accuracy is as high as 94.5%, which is 13.5% higher than that of the K-means model. The research method has been validated to improve the limitations of K-means mode, such as high screening error, weak stability, and falling into local optimal solutions. It optimizes the screening effect of drought resistant potato germplasm resources, which is conducive to exploring the potential of potato resources. In addition, research has also provided broader ideas for the optimization and application of particle swarm optimization algorithms.