@article{Zhang2025, 
author = {Weipeng Zhang and Hongze Guo and Bo Zhao and Liming Zhou and Fengzhu Wang and Dongyang Wang and Yangchun Liu},
title = {Peroxymonosulfate activation on dual iron sites for efficient antibiotic degradation: Synergistic effects of single-atom iron and iron nanoclusters},
year = {2025},
journal = {International Journal of Agricultural and Biological Engineering},
volume = {18},
number = {6},
pages = {202-211},
keywords = {combine harvester, working condition monitoring, GPS, production forecast, intelligent decision-making},
url = {https://www.sciopen.com/article/10.25165/j.ijabe.20251806.8780},
doi = {10.25165/j.ijabe.20251806.8780},
abstract = {Against the backdrop of precision agriculture and the development of intelligent agricultural machinery, current domestic monitoring systems for wheat combine harvesters are plagued by limited functionality, low intelligence, significant errors in parameter monitoring, and yield estimation results prone to inaccuracies. Specifically, they lag behind mature international systems in terms of fault warning accuracy, data transmission efficiency, and yield visualization capabilities. This study seeks to realize comprehensive and precise monitoring, reliable fault early warning, and intelligent yield prediction for wheat combine harvesters across all operating conditions. To this end, it innovatively adopts CAN bus integration technology and impulse-type grain flow sensors to develop a comprehensive system for monitoring the operational status and warning faults of wheat combine harvesters, which covers the entire operational process. By integrating GPS positioning, multi-sensor parameter acquisition, and intelligent analysis modules through CAN bus integration, the system enables unified monitoring of geographic information, operational data, cleaning loss, and fault status. Additionally, it incorporates a yield measurement module based on an impulse-type grain flow sensor to generate the real-time yield distribution maps. Field experiments demonstrate that the system achieves an alarm accuracy of 97.3%, controls the fuel consumption measurement error within 5%, and limits the relative error of yield measurement accuracy to no more than 4%. Notably, the impulse-type grain flow sensor exhibits stable static detection accuracy and rapid, precise dynamic measurement performance—laying a solid foundation for the automation and intelligent advancement of combine harvester technologies.}
}