Research on Intelligent Decision-Making Irrigation Model of Water and Fertilizer Based on Multi-source Data Input
文献类型： 外文期刊
作者： Li, Shanshan 1 ; Miao, Yisheng 1 ; Han, Xiao 1 ; Guo, Wei 1 ;
作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China
3.Minist Agr & Rural Affairs, Key Lab Digital Village Technol, Beijing 100097, Peoples R China
关键词： Water and fertilizer; Intelligence; Multi-source data; Irrigation decision
期刊名称：ARTIFICIAL INTELLIGENCE, CICAI 2022, PT II （ 影响因子：0.302； ）
ISSN： 0302-9743
年卷期： 2022 年 13605 卷
页码：
收录情况： SCI
摘要： At present, the integrated irrigation management and control system of water and fertilizer has met the requirements of automatic control of farmland water and fertilizer, gradually transforming the traditional manual operation into facility industrialization. However, this method has a weak use of data, and there is still a large gap between the calculation method and intelligent management and control. Taking greenhouse cabbage as the main research object, based on the cultivation environmental parameters, growth morphological parameters, water and fertilizer irrigation requirements during the growth period of cabbage, and using the efficient allocation ability of attention mechanism to data feature weights, this paper proposes the establishment of water and fertilizer intelligent decision-making management and control model integrating multi-source data input. The results showed that the prediction error of the intelligent decision-making irrigation model for water and fertilizer for greenhouse cabbage was relatively small, RMSE was 0.002447m(3)/Day, MAEis 0.001779m(3)/Day, and the coupling relationship between multi-source data is comprehensively analyzed, and the overall performance of model decision-making is improved through multi-feature extraction.
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