|Wilds: A benchmark of in-the-wild distribution shifts|
PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ...
International Conference on Machine Learning, 5637-5664, 2021
|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
|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
|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
|Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images|
W Guo, T Fukatsu, S Ninomiya
Plant methods 11, 1-15, 2015
|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
|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
|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
|Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods|
E David, M Serouart, D Smith, S Madec, K Velumani, S Liu, X Wang, ...
Plant Phenomics, 2021
|Extending the WILDS benchmark for unsupervised adaptation|
S Sagawa, PW Koh, T Lee, I Gao, SM Xie, K Shen, A Kumar, W Hu, ...
arXiv preprint arXiv:2112.05090, 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
|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
|UAS-based plant phenotyping for research and breeding applications|
W Guo, ME Carroll, A Singh, TL Swetnam, N Merchant, S Sarkar, ...
Plant Phenomics, 2021
|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, 1-16, 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, 2018
|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
|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
|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