Abstract: With corn cultivation areas expansion in China, mechanized harvesting efficiency has become crucial．However, current self-propelled corn harvesters still face issues such as low operational efficiency and high damage rates in practical applications．Key indicators for intelligent perception in self-propelled corn harvesters were analyzed, focusing on effects of four critical parameters including moisture content, loss rate, damage rate, and impurity rate on harvesting efficiency and quality．Moisture content detection primarily employed capacitance and microwave methods suitable for in-field online monitoring of corn kernels, while infrared techniques were briefly discussed for potential applications．Damage rate detection often adopted image recognition, flexible contact sensing, and simulation optimization technologies．Loss rate detection involved loading weighing, vision recognition, and cleaning structure optimization．Impurity rate detection mainly used visual inspection and laser particle size measurement．In future, self-propelled corn harvesters will evolve toward greater intelligence and precision by adopting advanced smart sensing technologies and automated control systems, while enhancing research into intelligent closed-loop control systems, to improve overall performance and harvesting efficiency．