Wei Guo(郭威)
Wei Guo(郭威)
在 g.ecc.u-tokyo.ac.jp 的电子邮件经过验证 - 首页
On plant detection of intact tomato fruits using image analysis and machine learning methods
K Yamamoto, W Guo, Y Yoshioka, S Ninomiya
Sensors 14 (7), 12191-12206, 2014
High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling
K Watanabe, W Guo, K Arai, H Takanashi, H Kajiya-Kanegae, ...
Frontiers in plant science 8, 421, 2017
Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model
W Guo, UK Rage, S Ninomiya
Computers and electronics in agriculture 96, 58-66, 2013
Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV
T Duan, B Zheng, W Guo, S Ninomiya, Y Guo, SC Chapman
Functional Plant Biology 44 (1), 169-183, 2016
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images
W Guo, T Fukatsu, S Ninomiya
Plant methods 11 (1), 1-15, 2015
A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting
S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ...
Plant Phenomics 2019, 2019
Aerial imagery analysis–quantifying appearance and number of sorghum heads for applications in breeding and agronomy
W Guo, B Zheng, AB Potgieter, J Diot, K Watanabe, K Noshita, DR Jordan, ...
Frontiers in plant science 9, 1544, 2018
Wilds: A benchmark of in-the-wild distribution shifts
PW Koh, S Sagawa, SM Xie, M Zhang, A Balsubramani, W Hu, ...
International Conference on Machine Learning, 5637-5664, 2021
EasyPCC: benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions
W Guo, B Zheng, T Duan, T Fukatsu, S Chapman, S Ninomiya
Sensors 17 (4), 798, 2017
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods
E David, S Madec, P Sadeghi-Tehran, H Aasen, B Zheng, S Liu, ...
Plant Phenomics 2020 (Article ID 3521852), https://doi.org/10.34133/2020/3521852, 2020
Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle
Y Mu, Y Fujii, D Takata, B Zheng, K Noshita, K Honda, S Ninomiya, W Guo
Horticulture research 5 (1), 1-10, 2018
Automatic estimation of heading date of paddy rice using deep learning
SV Desai, VN Balasubramanian, T Fukatsu, S Ninomiya, W Guo
Plant Methods 15, 76, 2019
Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding
P Hu, W Guo, SC Chapman, Y Guo, B Zheng
ISPRS Journal of Photogrammetry and Remote Sensing 154, 1-9, 2019
Active learning with point supervision for cost-effective panicle detection in cereal crops
AL Chandra, SV Desai, VN Balasubramanian, S Ninomiya, W Guo
Plant Methods 16 (1), 1-16, 2020
A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass
X Fan, K Kawamura, W Guo, TD Xuan, J Lim, N Yuba, Y Kurokawa, ...
Computers and Electronics in Agriculture 144, 314-323, 2018
An adaptive supervision framework for active learning in object detection
SV Desai, AL Chandra, W Guo, S Ninomiya, VN Balasubramanian
arXiv preprint arXiv:1908.02454, 2019
Node detection and internode length estimation of tomato seedlings based on image analysis and machine learning
K Yamamoto, W Guo, S Ninomiya
Sensors 16 (7), 1044, 2016
Computer vision with deep learning for plant phenotyping in agriculture: A survey
AL Chandra, SV Desai, W Guo, VN Balasubramanian
arXiv preprint arXiv:2006.11391, 2020
Easy MPE: Extraction of quality microplot images for UAV-based high-throughput field phenotyping
L Tresch, Y Mu, A Itoh, A Kaga, K Taguchi, M Hirafuji, S Ninomiya, W Guo
Plant Phenomics 2019, 2019
Intact detection of highly occluded immature tomatoes on plants using deep learning techniques
Y Mu, TS Chen, S Ninomiya, W Guo
Sensors 20 (10), 2984, 2020
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