Intelligent Systems for Combine Harvesters: A Comprehensive Review of Technologies and Trends
Li Jiang, Guanqun Wang, Boyan Xu, Naveed Husnain, Qi Wang
Abstract
As a key agricultural machine, the intelligent development of combine harvesters is vital for boosting operational efficiency, cutting resource use, and promoting sustainable agriculture. This review deeply analyzes the technological progress and application effects of intelligent combine harvester systems in five areas: autonomous navigation, adaptive operation control, hybrid power, fault monitoring and diagnosis, and intelligent cleaning. In autonomous navigation, multi-sensor fusion enables centimeter level precision in complex environments. For instance, YTO’s unmanned harvester in Henan’s wheat-growing area operated for eight hours straight, cutting loss rates by 15%. Adaptive operation control systems, by monitoring crop and machine-running data in real time and dynamically optimizing parameters, have reduced rice - harvesting grain loss from 3.5% to 1.2% in traditional models. Hybrid power systems lower fuel consumption by 42.4% per unit area compared to diesel powered ones and cut annual CO2 emissions by 8.6 tons. In fault monitoring and diagnosis, multi-modal sensor networks and data fusion strategies enhance monitoring accuracy, while AI driven fault models boost diagnostic precision. The IPSO-SVM fault diagnosis algorithm, for example, has a 97.96% accuracy rate. Intelligent cleaning systems, using multi-sensor perception, smart decision making algorithms, and precise actuators, have transitioned from experience based to data driven approaches, reducing loss rates by 15%.This review comprehensively sums up the current state, key technologies, application cases, and future trends of combine harvester intelligent systems. It offers a full reference for global agricultural machinery intelligence and promotes the automation, precision, and efficiency of agricultural production.