Use of two hybrid algorithms in the investigation and prediction of the values of five quantitative traits of guar beans in different deficit irrigation methods

Abstract

AbstractNowadays, deficit irrigation is of particular importance in areas facing the water shortage and drought. This study focused on the investigation and prediction of the values of five quantitative traits of guar beans under different deficit irrigation methods. Deficit irrigation methods were carried out at the initial, development, mid, and late plant growth stages. The experiment was carried out in 25 treatments each with four replications in 2018 and 2019. Initially, the values of five quantitative traits of guar beans were divided into three categories, the values of which were clustered using the K-means algorithm. Then, clusters were predicted using a combination of K-means and CART algorithms. Finally, the relationship between different deficit irrigation methods and clusters was investigated by a combination of K-means and Apriori algorithms. The results of two hybrid algorithms determined that the amount of irrigation in the mid-stage of plant growth significantly affected the five quantitative traits of guar beans. After the mid-stage of the plant growth, the amount of irrigation in the development, initial, and late growth stages had the greatest effect on the quantitative traits of guar beans. Among the deficit irrigation methods, irrigation rates of 60% in the primary stage, 80% in the development stage, 100% in the mid-stage, and 40% in the late stage of the plant growth were the best deficit irrigation methods in the four stages of growth.