Research on Intelligent Decision-Making Irrigation Model of Water and Fertilizer Based on Multi-source Data Input

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 0.002447 m3/Day, MAE is 0.001779 m3/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.