Irrigation decision method for winter wheat growth period in a supplementary irrigation area based on a support vector machine algorithm

Abstract

In supplementary irrigation areas, it is very important to formulate an irrigation plan based on the previous precipitation. However, because of the difficulty of accurately predicting the weather, most studies on irrigation decision making have recommended irrigation schemes under different historical weather years. In this study, a water-saving irrigation system for winter wheat based on the DSSAT model and a genetic algorithm was optimized for different historical years (1970–2017). Accordingly, a decision-making method for determining whether to irrigate in the growth stage of winter wheat was developed using a support vector machine algorithm based on the amount of precipitation in the early stage of winter wheat and the amount of irrigation. The results indicated that the decision-making accuracy in the wintering period, regreening period, and jointing period were 89.4%, 95.7%, and 93.6%, respectively. Thus, the proposed method can effectively determine the irrigation schemes corresponding to different growth stages of winter wheat and facilitate optimal decision making for water-saving irrigation in the winter wheat season in supplementary irrigation areas.