Browsing by Author "Tifton, G. A."

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Field testing of the autonomous cotton harvesting Roverin undefoliated cotton field
(2020 Beltwide Cotton Conferences, Austin, Texas, 2020) Fue, K. G.; Porter, W. M.; Tifton, G. A.; Barnes, E. M.; Cary, N. C.; Rains, G. C.
This study proposes the use of an autonomous rover to harvest cotton bolls before defoliation and as the bolls open. This would expand the harvest window to up to 50 days and make cotton production more profitable for farmers by picking cotton before the quality is at risk. We developed a cotton harvesting rover that is a center-articulated vehicle with an x- y picking manipulator and a combination vacuum and rotating tines end-effector to pull bolls off the plant. The rover uses a stereo camera to see rows, RTK-GPS to localize itself, fisheye camera for heading, and stereo camera to locate the cotton bolls. The SMACH library is a ROS-independent task-level architecture used to build state machines for the rapid implementation of the robot behavior. First, the GPS waypoints are obtained, and then, the rover passes over the rows while picking the cotton bolls. The navigation is controlled by a modified pure-pursuit technique together with a PID controller. Two parallel programs organize the entire rover regarding when to pick and when to navigate. While navigating, the rover looks for harvestable bolls, and when bolls are discovered, the robot will stop and pick. It will do this repetitive work until it finishes all the rows. The rover navigation had an absolute error mean of 0.189 m, a median of 0.172 m, a standard deviation of 0.137 m, and a maximum of 0.986 m. The largest errors occurred during turning around at the end of rows and were caused by wet conditions and tire slippage. The rover picked cotton bolls at the average Action Success Ratio (ASR) of 78.5% and was able to reach 95% of the bolls. Most bolls that were not picked could not be pulled into the vacuum using the rotating tines on the end-effector.
Real-time 3-D measurement of cotton boll positions using machine vision under field conditions
(2018 Beltwide Cotton Conferences, San Antonio, 2018-01) Fue, K. G.; Rains, G. C.; Porter, W. M.; Tifton, G. A.
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.