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To address the issues of fertilizer resource wastage and the inability to achieve spatially differentiated nutrient supply in rice side-deep fertilization due to the lack of scientific prescriptions and intelligent decision-making, a prescription map-based variable-rate fertilization control system for rice side-deep fertilization was designed. Combining fertilizer application theory analysis, controllable factors influencing the fertilizer application rate in variable rate fertilization operations, including fertilizer shaft rotation speed, external groove wheel working length, and machine forward speed were identified to explore fertilization control strategies and construct and train an intelligent decision-making model based on neural networks. Using the soil nutrient balance method combined with the Kriging spatial interpolation method in ArcGIS, a fertilization prescription map was generated. Subsequently, the hardware and software for the rice side-deep variable rate fertilization control system were designed and integrated into a rice transplanter for performance testing. The precision test of the bivariate control model for fertilizer amount showed a maximum application error of 3.27%, a minimum error of 0.06%, and an overall average error of 1.23%. Results from the target fertilization amount accuracy test indicated that the average application rate errors for urea, diammonium phosphate, potassium sulfate, and blended compound fertilizer were 4.60%, 4.41%, 4.18%, and 3.66%, respectively, with average dosage errors of 5.04%, 4.83%, 3.81%, and 4.84%. In field tests guided by electronic prescription maps, the system obtained 54 867 valid positioning points with six positioning errors, all occurring near field boundaries, the average broadcasting amount error for partitioned and fixed-point variable fertilization was 4.23%. The results demonstrated that the integrated rice transplanter with side-deep variable rate fertilization capabilities had high application rate accuracy and stability, and the control system exhibited high precision in fertilizer amount control. Using electronic prescription maps to guide fertilization operations was practical and reliable, enabling integrated intelligent variable-rate fertilization during rice transplanting.