Department of Agricultural Engineering

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Browsing Department of Agricultural Engineering by Subject "Cotton harvesting"

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    Center-articulated hydrostatic cotton harvesting Rover using visual-servoing control and a finite state machine
    (MDPI, 2020-07-30) Kadeghe, Fue; Wesley, Porter; Edward, Barnes; Changying, Li; Glen, Rains
    Multiple small rovers can repeatedly pick cotton as bolls begin to open until the end of the season. Several of these rovers can move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To develop such a multi-agent cotton-harvesting system, each cotton-harvesting rover would need to accomplish three motions: the rover must move forward/backward, turn left/right, and the robotic manipulator must move to harvest cotton bolls. Controlling these actions can involve several complex states and transitions. However, using the robot operating system (ROS)-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability for deploying multiple tasks to the rover and manipulator. In this study, a center-articulated hydrostatic cotton-harvesting rover, using a stereo camera to locate end-effector and pick cotton bolls, was developed. The robot harvested the bolls by using a 2D manipulator that moves linearly horizontally and vertically perpendicular to the direction of the rover’s movement. We demonstrate preliminary results in an environment simulating direct sunlight, as well as in an actual cotton field. This study contributes to cotton engineering by presenting a robotic system that operates in the real field. The designed robot demonstrates that it is possible to use a Cartesian manipulator for the robotic harvesting of cotton; however, to reach commercial viability, the speed of harvest and successful removal of bolls (Action Success Ratio (ASR)) must be improved.
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    Deep learning based Real-time GPU-accelerated tracking and counting of cotton bolls under field conditions using a moving camera
    (2018 ASABE Annual International Meeting, 2018-08) Fue, Kadeghe G.; Porter, Wesley; Rains, Glen
    Robotic harvesting involves navigation and environmental perception as first operations before harvesting of the bolls can commence. Navigation is the distance required for a harvester’s arm to reach the cotton boll while perception is the position of the boll relative to surrounding environment. These two operations give a 3D position of the cotton boll for picking and can only be achieved by detection and tracking of the cotton bolls in real-time. It means detection, tracking and counting of cotton bolls using a moving camera allows the robotic machine to harvest easily. GPU-accelerated deep neural networks were used to train the convolution networks for detection of cotton bolls. It was achieved by using pretrained tiny yolo weights and DarkFlow, a framework which translates YOLOv2 darknet neural networks to TensorFlow. A method to connect tracklets using vectors that are predicted using Lucas-Kanade algorithm and optimized using robust L-estimators and homography transformation is proposed. The system was tested in defoliated cotton plants during the spring of 2018. Using three video treatments, the counting performance accuracy was around 93% with standard deviation 6%. The system average processing speed was 21 fps in desktop computer and 3.9 fps in embedded system. Detection of the system achieved an accuracy and sensitivity of 93% while precision was 99.9% and F1 score was 1. The Tukey’s test showed that the system accuracy and sensitivity was the same when the plants were rearranged. This performance is crucial for real-time robot decisions that also measure yield while harvesting.
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    An extensive review of mobile agricultural robotics for field operations: focus on cotton harvesting
    (MDPI, 2020-03-04) Kadeghe, G. Fue; Barnes, Edward M.; Rains, Glen C; Porter, Wesley M
    In this review, we examine opportunities and challenges for 21st-century robotic agricultural cotton harvesting research and commercial development. The paper reviews opportunities present in the agricultural robotics industry, and a detailed analysis is conducted for the cotton harvesting robot industry. The review is divided into four sections: (1) general agricultural robotic operations, where we check the current robotic technologies in agriculture; (2) opportunities and advances in related robotic harvesting fields, which is focused on investigating robotic harvesting technologies; (3) status and progress in cotton harvesting robot research, which concentrates on the current research and technology development in cotton harvesting robots; and (4) challenges in commercial deployment of agricultural robots, where challenges to commercializing and using these robots are reviewed. Conclusions are drawn about cotton harvesting robot research and the potential of multipurpose robotic operations in general. The development of multipurpose robots that can do multiple operations on different crops to increase the value of the robots is discussed. In each of the sections except the conclusion, the analysis is divided into four robotic system categories; mobility and steering, sensing and localization, path planning, and robotic manipulation.
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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.
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    Visual control of cotton-picking Rover and manipulator using a ROS-independent finite state machine
    (2019 ASABE Annual International Meeting, 2019-07) Fue, Kadeghe; Barnes, Edward; Porter, Wesley; Rains, Glen
    Small rovers are being developed to pick cotton as bolls open. The concept is to have several of these rovers move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To accomplish this goal, each cotton-picking robot needs to accomplish three movements; rover must move forward/backward, left/right and the manipulator must be able to move to harvest the detected cotton bolls. Control of these actions can have several states and transitions. Transitions from one state to another can be complex but using ROS-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability to deploy multiple tasks to the rover and manipulator. In this research, a cotton-picking robot using a stereo camera to locate end-effector and cotton bolls is developed. The robot harvests the bolls using a 2D manipulator that moves linearly horizontally and vertically. The boll 3-D position is determined by calculating stereo camera parameters, and the decision of the finite state machine guides the manipulator and the rover to the destination. PID control is deployed to control rover movement to the boll. We demonstrate preliminary results in a direct-sun simulated environment. The system achieved a picking performance of 17.3 seconds per boll. Also, it covered the task by navigating at a speed of 0.87 cm per second collecting 0.06 bolls per second. In each mission, the system was able to detect all the bolls but one.