| 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 | 1461 | 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 | 310 | 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 | 304 | 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 | 240 | 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 | 236 | 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 | 182 | 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 | 145 | 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 | 131 | 2016 |
| 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 | 128 | 2021 |
| 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 | 118* | 2021 |
| UAS-based plant phenotyping for research and breeding applications W Guo, ME Carroll, A Singh, TL Swetnam, N Merchant, S Sarkar, ... Plant Phenomics, 2021 | 103 | 2021 |
| 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 | 100 | 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 | 97 | 2020 |
| 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 | 94 | 2018 |
| 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 | 80 | 2017 |
| Deep-learning-based in-field citrus fruit detection and tracking W Zhang, J Wang, Y Liu, K Chen, H Li, Y Duan, W Wu, Y Shi, W Guo Horticulture Research 9, uhac003, 2022 | 77 | 2022 |
| 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 | 75 | 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 | 72 | 2018 |
| 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 | 70 | 2020 |
| Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data Z Li, Y Zhao, J Taylor, R Gaulton, X Jin, X Song, Z Li, Y Meng, P Chen, ... Remote Sensing of Environment 273, 112967, 2022 | 67 | 2022 |
Seishi NinomiyaThe University of TokyoVerified email at g.ecc.u-tokyo.ac.jp
