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To mitigate the issues of uncertainty disturbances and time delays faced by corn harvesting machines in complex and time-varying operational environments, and improve the disturbance rejection capability of the control system as well as the quality of corn harvesting, an adaptive control method based on disturbance observation was proposed for corn ear-picking harvesting. This method targeted to minimize the ear loss rate and utilized the rotational speed of the pulling rollers, operating speed, and header height as the primary control subsystems. Firstly,，tailored to the operational environment and system characteristics of the harvester, models of the corn ear-picking system were constructed. Secondly, a parallel weighted PI controller was employed to optimize the desired target values for each subsystem. Thirdly, utilizing active disturbance rejection control （ADRC） as the feedback mechanism, an extended state observer was implemented to estimate and compensate for both internal and external disturbances in the system online;Finally, an adaptive control system model for the harvester was established, and simulation experiments were conducted to evaluate the effectiveness of the harvester system control strategy and the control performance of each subsystem. Field experiments were also conducted to verify the effectiveness of the control strategy. Experimental results demonstrated that all subsystems collaborated seamlessly according to predefined control rules, achieving control objectives quickly and stably with steady-state errors eventually converging to zero. The system exhibited strong anti-disturbance capabilities and achieved satisfactory control effects.