Design of precise fertilization method for greenhouse vegetables based on improved backpropagation neural network

The traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, the main goal of this study is to design a precise fertilization method for greenhouse vegetables based on the improved back-propagation neural network (IM-BPNN) algorithm to increase fertilizer utilization efficiency, reduce production costs, and improve the economic viability of agriculture. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score (zero-mean normalization) standardization method. Then, the BPNN (backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY (nutrient balance target yield),BPNN (backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. Compared with the BPNN and NBTY algorithm, it shows that the IM-BPNN algorithm can more accurately determine the amount of fertilizer required by vegetables and avoid over-application, which can improve fertilizer utilization efficiency, reduce production costs, and improve the economic feasibility of agriculture.