Decision-making technology based on knowledge engineering and experiment on the intelligent water-fertilizer irrigation system

Abstract

Although the irrigation technologies based on the Decision-making System (DMS) began in the late 1990s, while being merely embryonic from laboratory research into application in the agricultural irrigation areas, DMS based on intelligent algorithms have drawn much attention from the academia over the recent years. In this study, we have provided an overview of the decision-making technology based on knowledge engineering for intelligent irrigation system referred to as Knowledge-based Engineering (KBE). As the modern technical research and scientific theory on agricultural water saving is further developed, the water-fertilizer irrigation is becoming increasingly intelligent. We have put forward the concept of KBE intelligent irrigation system and its support to decision-making in the study, while adopting the techniques and methods of knowledge engineering. In addition, we have combined our research findings with the expert knowledge on the water-fertilizer irrigation in a system integrated with computer network, intelligent reasoning and artificial intelligence (AI), among other modern high-techs. We have set up the decision-making models and analytical methods of irrigation and fertilization for KBE by referring to the expert experience and data of fertilization. Moreover, we have taken into account the web crawler technology in irrigation and fertilization, and we have put forward novel methods of knowledge acquisition based on the web crawler. Correspondingly, we have established the knowledge base for the decision-making support system tailored to irrigation and fertilization.

The experiment result shows that the recommended irrigation quota is compared with local cultivation technology experience to obtain a decision accuracy of 81.7%. And the water and fertilizer management plan obtained by the intelligent decision-making system has a thicker stem and higher plant height during the growth period than the crops obtained by local cultivation experience. The output of the decision-making system is 620 kg, which a relative increase of 5.08% is compared with the 590 kg obtained from local cultivation experience.

Keywords

Knowledge-based engineering decision-making support intelligent irrigation artificial intelligence

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