Department of Agricultural Engineering

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Browsing Department of Agricultural Engineering by Subject "Cotton Bolls"

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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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    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.