| Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images Q Yang, L Shi, J Han, Y Zha, P Zhu Field Crops Research 235, 142-153, 2019 | 300 | 2019 |
| A near real-time deep learning approach for detecting rice phenology based on UAV images Q Yang, L Shi, J Han, J Yu, K Huang Agricultural and Forest Meteorology 287, 107938, 2020 | 114 | 2020 |
| Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations D Yu, Y Zha, L Shi, X Jin, S Hu, Q Yang, K Huang, W Zeng European Journal of Agronomy 121, 126159, 2020 | 62 | 2020 |
| Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation S Hu, L Shi, K Huang, Y Zha, X Hu, H Ye, Q Yang Field Crops Research 232, 49-61, 2019 | 54 | 2019 |
| Real-time detection of rice phenology through convolutional neural network using handheld camera images J Han, L Shi, Q Yang, K Huang, Y Zha, J Yu Precision Agriculture 22, 154-178, 2021 | 36 | 2021 |
| Estimation of leaf area index of sugarcane using crop surface model based on UAV image Q Yang, H Ye, K Huang, Y Zha, L Shi Transactions of the Chinese Society of Agricultural Engineering 33 (8), 104-111, 2017 | 24 | 2017 |
| A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images Q Yang, L Shi, J Han, Z Chen, J Yu Field Crops Research 277, 108419, 2022 | 20 | 2022 |
| Rice yield estimation using a CNN-based image-driven data assimilation framework J Han, L Shi, Q Yang, Z Chen, J Yu, Y Zha Field Crops Research 288, 108693, 2022 | 17 | 2022 |
| Plot-scale rice grain yield estimation using UAV-based remotely sensed images via CNN with time-invariant deep features decomposition Q Yang, L Shi, L Lin IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019 | 10 | 2019 |
| A scalable framework for quantifying field-level agricultural carbon outcomes K Guan, Z Jin, B Peng, J Tang, EH DeLucia, P West, C Jiang, S Wang, ... Earth-Science Reviews, 104462, 2023 | 8* | 2023 |
| Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data L Zhao, Q Yang, Q Zhao, J Wu Remote Sensing 13 (20), 4057, 2021 | 7 | 2021 |
| Estimation of Winter Wheat Leaf Water Content Based on Leaf and Canopy Hyperspectral Data C Xiu-qing, Y Qi, H Jing-ye, L Lin, S Liang-sheng Spectroscopy and Spectral Analysis 40 (3), 891-897, 2020 | 6 | 2020 |
| DeepOryza: A Knowledge guided machine learning model for rice growth simulation J Han, L Shi, C Pylianidis, Q Yang, IN Athanasiadis 2nd AAAI Workshop on AI for Agriculture and Food Systems, 2023 | 4 | 2023 |
| A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q Yang, L Liu, J Zhou, R Ghosh, B Peng, K Guan, J Tang, W Zhou, ... Remote Sensing of Environment 299, 113880, 2023 | 3 | 2023 |
| Assessing parametric and nitrogen fertilizer input uncertainties in the ORYZA_V3 model predictions J Yu, L Shi, J Han, Q Yang, J Huang, M Ye Agronomy Journal 113 (6), 4965-4981, 2021 | 3 | 2021 |
| A deep transfer learning framework for mapping high spatiotemporal resolution LAI J Zhou, Q Yang, L Liu, Y Kang, X Jia, M Chen, R Ghosh, S Xu, C Jiang, ... ISPRS Journal of Photogrammetry and Remote Sensing 206, 30-48, 2023 | 1 | 2023 |
| Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity Q Yang, L Shi, J Han, Y Zha, J Yu, W Wu, K Huang Field Crops Research 293, 108847, 2023 | 1 | 2023 |
| Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Q Yang, L Liu, J Zhou, M Rogers, Z Jin Computers and Electronics in Agriculture 220, 108911, 2024 | | 2024 |
Liangsheng ShiProfessor of Soil Hydrology and Agricultural Water Resource Management, Wuhan UniversityVerified email at whu.edu.cn
