Cotton harvesting is performed by expensive combine harvesters that hinder small to medium-size cotton farmers Advances in robotics provide an opportunity to harvest cotton using small and robust autonomous rovers that can be deployed in the field as an “army” of harvesters. This paradigm shift in cotton harvesting requires high accuracy 3D measurement of the cotton boll position under field conditions. This in-field high throughput phenotyping of cotton boll position includes real-time image acquisition, depth processing, color segmentation, features extraction and determination of cotton boll position. In this study, a 3D camera system was mounted on a research rover at 82° below the horizontal and took 720p images at the rate of 15 frames per second while the rover was moving over 2-rows of potted defoliated cotton plants. The software development kit provided by the camera manufacturer was installed and used to process and provide a disparity map of cotton bolls. The system was installed with the Robot Operating System (ROS) to provide live image frames to client computers wirelessly and in real time. Cotton boll distances from the ground were determined using a 4-step machine vision algorithm (depth processing, color segmentation, feature extraction and frame matching for position determination). The 3D camera used provided distance of the boll from the left lens and algorithms were developed to provide vertical distance from the ground and horizontal distance from the rover. Comparing the cotton boll distance above the ground with manual measurements, the system achieved an average R2 value of 99% with 9 mm RMSE when stationary and 95% with 34 mm RMSE when moving at approximately 0.64 km/h. This level of accuracy is favourable for proceeding to the next step of simultaneous localization and mapping of cotton bolls and robotic harvesting.Cotton incorporated