| A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level H Jiang, H Hu, R Zhong, J Xu, J Xu, J Huang, S Wang, Y Ying, T Lin Global change biology 26 (3), 1754-1766, 2020 | 157 | 2020 |
| DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping J Xu, Y Zhu, R Zhong, Z Lin, J Xu, H Jiang, J Huang, H Li, T Lin Remote Sensing of Environment 247, 111946, 2020 | 147 | 2020 |
| DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation T Lin, R Zhong, Y Wang, J Xu, H Jiang, J Xu, Y Ying, L Rodriguez, ... Environmental research letters 15 (3), 034016, 2020 | 55 | 2020 |
| Double cropping and cropland expansion boost grain production in Brazil J Xu, J Gao, HV de Holanda, LF Rodríguez, JV Caixeta-Filho, R Zhong, ... Nature Food 2 (4), 264-273, 2021 | 32 | 2021 |
| Large-scale rice mapping using multi-task spatiotemporal deep learning and sentinel-1 sar time series Z Lin, R Zhong, X Xiong, C Guo, J Xu, Y Zhu, J Xu, Y Ying, KC Ting, ... Remote Sensing 14 (3), 699, 2022 | 20 | 2022 |
| The quantitative models for broiler chicken response to monochromatic, combined, and mixed light-emitting diode light: A meta-analysis Y Yang, C Pan, R Zhong, J Pan Poultry science 97 (6), 1980-1989, 2018 | 18 | 2018 |
| Artificial light and biological responses of broiler chickens: dose-response Y Yang, C Pan, R Zhong, J Pan Journal of Animal Science 96 (1), 98-107, 2018 | 14 | 2018 |
| A spatiotemporal assessment of field residues of rice, maize, and wheat at provincial and county levels in China T Lin, J Xu, X Shen, H Jiang, R Zhong, S Wu, Z Du, L Rodriguez, KC Ting GCB Bioenergy 11 (10), 1146-1158, 2019 | 12 | 2019 |
| Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning Z Lin, R Fu, G Ren, R Zhong, Y Ying, T Lin Frontiers in Plant Science 13, 980581, 2022 | 9 | 2022 |
| Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning R Zhong, Y Zhu, X Wang, H Li, B Wang, F You, LF Rodríguez, J Huang, ... Fundamental Research 3 (6), 951-959, 2023 | 5 | 2023 |
| Efficient Multi-temporal and In-season Crop Mapping with Landsat Analysis Ready Data via Long Short-term Memory Networks J Xu, R Zhong, J Xu, H Li, J Huang, T Lin | 1 | 2019 |
| Daily DeepCropNet: A hierarchical deep learning approach with daily time series of vegetation indices and climatic variables for corn yield estimation X Xiong, R Zhong, Q Tian, J Huang, L Zhu, Y Yang, T Lin ISPRS Journal of Photogrammetry and Remote Sensing 209, 249-264, 2024 | | 2024 |
| Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning X Xiong, J Yang, R Zhong, J Dong, J Huang, KC Ting, Y Ying, T Lin Computers and Electronics in Agriculture 216, 108487, 2024 | | 2024 |
| Crop yield estimation method based on deep temporal and spatial feature combined learning T Lin, R Zhong, XU Jinfan, H Jiang, Y Ying, KC Ting US Patent 11,741,555, 2023 | | 2023 |
| Spectral Model for Broiler Chicken Production Y Yang, C Pan, R Zhong, J Pan Animal Environment and Welfare, 2017 | | 2017 |
Tao LinZhejiang UniversityVerified email at zju.edu.cn
