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The Asian citrus psyllid (ACP) serves as the primary vector for Huanglongbing (HLB), a citrus tree disease with potentially devastating consequences for citrus orchards. In order to achieve efficient monitoring of ACP populations, an intelligent monitoring system capable of insect trapping, pest identification, and result visualization was developed. A monitoring device equipped with an automatic renewal mechanism for the insect trapping tape and real-time image capturing was designed. To improve the performance of the YOLO v8 model for ACP recognition, targeted cropping and Mosaic data augmentation techniques were employed to effectively expand the ACP dataset, addressing issues related to limited sample size and constrained positioning in the datasets. The application of a coordinate attention (CA) mechanism guided the model to comprehensively consider both channel and spatial information, thereby enhancing its ability to accurately locate the target psyllids. Additionally, the Web interface and mobile APP were developed to enable data visualization and remote control. During the model testing phase, the improved YOLO v8-MC achieved significant better performance than the baseline model, reaching 91.20%, 91%, and 90.60% in terms of recall rate, F1 score, and precision, respectively. In the field experiment, the model exhibited a recall rate of 88.64%, an F1 score of 87% and a precision of 84.78%, and the system operated effectively, meeting the requirements for field applications. In conclusion, the intelligent monitoring system developed enabled remote monitoring of ACP populations in orchards, providing an efficient mehtod for the management and control of such pest infestations.