By turning thousands of field and laboratory images into detections, counts and interaction events, this study explored how computer vision can reveal arthropod communities in action. It used computer vision to extract ecological information from images of arthropods in tropical agroecosystems. The approaches combined camera-based monitoring, YOLO-type object detection models, convolutional neural networks and hierarchical classification to detect, count and identify small arthropods, including visually similar taxa. Applied to systems involving ants, banana weevils, mealybugs, predators and associated arthropod communities, these tools described community composition, abundance-activity patterns, dominance hierarchies and day-night partitioning. They also quantified ecological interactions such as resource discovery, recruitment dynamics, bait monopolization, ant-mealybug mutualism, predator-prey contacts and changes in interaction networks. Hierarchical classification improved detection and identification when organisms were small, morphologically close or unevenly represented in training datasets. By linking field observation, laboratory experiments and automated image analysis, these approaches provided a concrete framework for studying arthropod communities, behaviours and ecological interactions at fine spatial and temporal scales. Beyond these case studies, this work highlights how computer vision can expand the way acarologists and arthropod ecologists observe small organisms, moving from static species records to dynamic, interaction-based ecology.