Yu Jiang
9 Papers
Yu Jiang is an academic researcher. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 1, co-authored 1 publications.
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Papers
Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms
TL;DR: The sensing systems employed for 2-D and 3-D road data acquisition, including camera(s), laser scanners, and Microsoft Kinect are introduced, and the SoTA computer vision algorithms developed for road pothole detection are reviewed.
Deep semantic segmentation for the quantification of grape foliar diseases in the vineyard
TL;DR: In this article , an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases using a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization.
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High throughput saliency-based quantification of grape powdery mildew at the microscopic level for disease resistance breeding
Tian Qiu,Anna Underhill,Surya Sapkota,Lance Cadle-Davidson,Yu Jiang +4 more
TL;DR: In this article , a saliency-based processing pipeline was developed for the quantification of grapevine powdery mildew (PM) infection in microscopic images and comprehensively evaluated its performance for genetic analyses.
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Vision-based Vineyard Navigation Solution with Automatic Annotation
TL;DR: In this paper , a vision-based autonomous navigation framework for agriculture robots in trellised cropping systems such as vineyards is proposed, where a learning-based method is proposed to estimate the path traversibility heatmap directly from an RGB-D image and subsequently convert the heatmap to a preferred traversal path.
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3D characterization of tree architecture for apple crop load estimation
Tian Qiu,Lailiang Cheng,Yu Jiang +2 more
- 01 Jan 2022
TL;DR: In this paper , a 3D point cloud-based approach for apple tree segmentation and architecture characterization was developed, where a biological-constrained graph-based algorithm was developed to segment the trunk and branch and characterize the architectural phenotypic traits of apple trees.
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