This study investigates developing and deploying of multiple machine vision systems and synchronizing them on a wide boom sprayer. Each machine vision system includes a pipeline that leverages YOLOv5l to enable real-time detection of beetles for spot spraying applications. 30 RGB cameras were mounted on the sprayer’s boom. The machine vision pipeline was developed in two primary phases: training and testing. During the training phase, images of beetles were collected to create a dataset comprising 328 training, 41 validation, and 42 testing images. Next, an electronic control unit (ECU) was developed to synchronize multiple machine vision systems with nozzles. To consolidate detection data from all cameras into a single processing unit compatible with the ISOBUS protocol. The ECU receives protocol messages containing detection information. Field tests confirmed the system’s performance and reliability. Evaluations involved a Case IH Patriot 3240 sprayer with 60 individually controlled nozzles and a custom sprayer mounted on a tractor with 27 nozzles. Experiments simulated various target distribution patterns, including individual targets, intersections, and bulk distributions. The ECU dynamically adjusted nozzle opening times based on vehicle speeds of 3.22 km/h, 6.44 km/h, and 9.66 km/h, maintaining consistent spray lengths of 71.52 cm across all scenarios. This standardized ECU enables the seamless synchronization of multiple machine vision systems with nozzles on wide boom sprayers, facilitating precision spot spraying with enhanced efficiency and reduced environmental impact.