Abstract: Abstract: Planting density and fertilizer application rate are the important factors affecting crop yield, and the unreasonable utilization has caused a series of serious consequences such as high cost, agriculture resources waste, agricultural non-point source pollution, and ecological environment deterioration and so on. In this study, a BP neural network-based optimization method of planting density and fertilizer application rate was proposed and tested for its feasibility by field experiments. The field experiment was carried out in Hongxing Farm of Heilongjiang, China (126°47′E, 48°01′N) in 2014 and 2015. The experiment of 4 factors and 5 levels was designed by using the quadratic orthogonal rotation method. Four factors included planting density, N, P and K application rate. Five levels were considered as the equally spaced values taken from the planting density of 6.86×104-10.78×104 plants/hm2, the N application rate of 40-216 kg/hm2, the P2O5 application rate of 32.2-151.8 kg/hm2 and the K2O application rate of 25-115 kg/hm2. Among the 5 levels, the 0 level referred to the local experience value. A total of 36 plots were prepared and each plot had the width of 4.4 m and the length of 5 m. Maize (variety of Deyamei No.1 ) was planted on ridges in the width of 100 cm and in the height of 15 cm. Irrigation was not conducted during the experiment. The rainfall during the growing season of maize was 471.4 mm in 2014 and 460.7 mm in 2015. At harvest, the maize yield was determined. The field data was fitted using BP neural network model and regression method, respectively for optimization of planting density and fertilizer application rates. The BP neural work optimization method included model establishment and global optimization. The data was processed in Matlab. The results showed that the BP neural network model had higher determination coefficient of 0.98 (P<0.01) than the regression model (R2=0.87, P<0.05). Meanwhile, the former had smaller root-mean-square error of 189.89 kg/hm2 than the latter (464.25 kg/hm2). It indicates that the BP neural network model was better in fitting the relationship between maize yield and fertilizer application rate. Furthermore, the global optimization was conducted for 10 times by using BP neural network model. Each computation started from random input values of planting density and fertilizer application rate within the designed range in the field experiment. All the computation provided a same optimization result: the maize yield of 16 308.53 kg/hm2, the planting density of 9.32×104 plants/hm2, the N application rate of 139.5 kg/hm2, the P2O5 application rate of 85.4 kg/hm2 and the K2O application rate of 70.8 kg/hm2. In 2016, a field experiment was conducted in the same place with the optimization results of the planting density and fertilizer application rate. The results showed that the maize yield was 15 948.3 kg/hm2 with a maximum value of 16 171.7 kg/hm2and a minimum value of 15 798.8 kg/hm2. The relative error between the measured and optimized maize yield was -2.21%, below than 5%. It suggested that the BP neural network model was reliable. Therefore, this study provides a stable and feasible optimization method to solve the similar optimization problem in field of agriculture production. Meanwhile, this study expands the application of BP neural network in agriculture.