<![CDATA[The escalation of pest attacks on rice commodities is a serious threat that requires immediate mitigation response. The main problem found in the field is the slow flow of information due to a manual reporting system that is fragmented between farmers and the agricultural department administration. This study aims to design an integrated geographic information system that combines object detection capabilities based on artificial intelligence on mobile devices with a web-based complaint management dashboard. The research location was conducted at the North Sulawesi Protection Center for Food Crops and Horticulture (BPPMTPH). The method used was Prototyping with a focus on modeling spatial data flows and system functionality testing analysis techniques. The proposed technical solution implements the You Only Look Once (YOLO) algorithm through a cloud-based application programming interface for automatic pest classification. The results of the study showed that the built client-server architecture was able to integrate geographic labeling features accurately. The implementation of the Marker Clustering method on the web side proved effective in simplifying the visualization of dense pest distribution data into systematic information. This system successfully eliminated the need for manual location authentication, allowing damage metrics and field coordinate points to be distributed to the central database instantly.Data alignment between visual reports and statistics of this area provides strong support for authorities in making more responsive pest control decisions.]]>
