| 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 | 318 | 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 | 124 | 2020 |
| 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 | 39 | 2021 |
| 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 | 20 | 2022 |
| 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 |
| 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 | 4 | 2023 |
| 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 |
| 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 |
