Bagworms are the main species of vicious leaf eating insect in oil palm plantation and poses serious threats to productivity. The economic impact from a moderate bagworm attack of 10%-50% leaf damage may cause approximately 43% yield loss. More than usual, the bagworm population often increases to above its threshold limits. If no control measures are taken, it will usually lead to a severe outbreak. Aware of this impact, detection and countermeasures of the bagworm populations are required as preliminary steps to ensure proper planning of control actions in the infested areas. Through an image processing analysis and integration of hardware and software approaches, the world’s first prototype known as Oto-BaCTM (Automatic bagworm counter) has been developed to detect and count the bagworms automatically in the field. It can detect bagworms according to three specific groups: Group 1: larval stage 1-3; Group 2: larval stage 4-7 and Group 3: pupal stage. In addition, the Oto-BaCTM is programmed to detect the living and dead larvae and pupae, which corresponds to motion-tracking and false colour analysis. Based on several field trials at three different locations, the results showed that the percentages of detection accuracy for the living and dead G1 larvae were recorded at 87.5% and 78.7%, respectively. As for the G2 larvae, the percentage of detection accuracy was 79.2%, for the living while dead larvae was at 79.2%. Meanwhile, the detection accuracy for Group 3 exhibited 77% and 75%, for the living and dead pupae separately. Hence, the application of an Oto-BaCTM is currently recommended to assist planters in automating census work in oil palm plantations.