Liang Wan
Liang Wan
Seoul National University
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Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer–a case study of small farmlands in the South of China
L Wan, H Cen, J Zhu, J Zhang, Y Zhu, D Sun, X Du, L Zhai, H Weng, Y Li, ...
Agricultural and Forest Meteorology 291, 108096, 2020
Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape
L Wan, Y Li, H Cen, J Zhu, W Yin, W Wu, H Zhu, D Sun, W Zhou, Y He
Remote Sensing 10 (9), 1484, 2018
Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras
H Cen, L Wan, J Zhu, Y Li, X Li, Y Zhu, H Weng, W Wu, W Yin, C Xu, ...
Plant Methods 15, 1-16, 2019
Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure
A Abdalla, H Cen, L Wan, R Rashid, H Weng, W Zhou, Y He
Computers and electronics in agriculture 167, 105091, 2019
Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality
D Sun, H Cen, H Weng, L Wan, A Abdalla, AI El-Manawy, Y Zhu, N Zhao, ...
Plant methods 15, 1-16, 2019
Nutrient status diagnosis of infield oilseed rape via deep learning-enabled dynamic model
A Abdalla, H Cen, L Wan, K Mehmood, Y He
IEEE Transactions on Industrial Informatics 17 (6), 4379-4389, 2020
Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets
L Wan, W Zhou, Y He, TC Wanger, H Cen
Remote Sensing of Environment 269, 112826, 2022
Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model
L Wan, J Zhang, X Dong, X Du, J Zhu, D Sun, Y Liu, Y He, H Cen
Computers and Electronics in Agriculture 187, 106304, 2021
Current status and future perspective of the application of deep learning in plant phenotype research
H Cen, Y Zhu, D Sun, L Zhai, L Wan, ZH Ma, Z Liu, Y He
Trans. Chin. Soc. Agric. Eng 36 (9), 1-16, 2020
Color calibration of proximal sensing RGB images of oilseed rape canopy via deep learning combined with K-means algorithm
A Abdalla, H Cen, E Abdel-Rahman, L Wan, Y He
Remote Sensing 11 (24), 3001, 2019
A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles
L Wan, J Zhu, X Du, J Zhang, X Han, W Zhou, X Li, J Liu, F Liang, Y He, ...
Journal of experimental botany 72 (13), 4691-4707, 2021
Spatiotemporal Heterogeneity of Chlorophyll Content and Fluorescence Response Within Rice (Oryza sativa L.) Canopies Under Different Nitrogen Treatments
J Zhang, L Wan, C Igathinathane, Z Zhang, Y Guo, D Sun, H Cen
Frontiers in plant science 12, 645977, 2021
PROSDM: Applicability of PROSPECT model coupled with spectral derivatives and similarity metrics to retrieve leaf biochemical traits from bidirectional reflectance
L Wan, J Zhang, Y Xu, Y Huang, W Zhou, L Jiang, Y He, H Cen
Remote Sensing of Environment 267, 112761, 2021
Multi-temporal monitoring of leaf area index of rice under different nitrogen treatments using UAV images
X Du, L Wan, H Cen, S Chen, J Zhu, H Wang, Y He
International Journal of Precision Agricultural Aviation 3 (1), 2020
Upscaling from leaf to canopy: Improved spectral indices for leaf biochemical traits estimation by minimizing the difference between leaf adaxial and abaxial surfaces
L Wan, Z Tang, J Zhang, S Chen, W Zhou, H Cen
Field Crops Research 274, 108330, 2021
Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor
W Liang, C Haiyan, Z Jiangpeng, Z Jiafei, D Xiaoyue, H Yong
Smart Agriculture 2 (1), 58, 2020
Characterization and detection of leaf photosynthetic response to citrus Huanglongbing from cool to hot seasons in two orchards
H Weng, Y Zeng, H Cen, M He, Y Meng, Y Liu, L Wan, H Xu, H Li, H Fang, ...
Transactions of the ASABE 63 (2), 501-512, 2020
Rapid detection of citrus Huanglongbing based on chlorophyll fluorescence imaging technology
H Weng, C He, J Xu, L Liu, J Qing, L Wan, D Ye
Trans. CSAE 36, 196-203, 2020
Combining UAV-based vegetation indices, canopy height and canopy coverage to improve rice yield prediction under different nitrogen levels
L Wan, H Cen, J Zhu, Y Li, Y Zhu, D Sun, H Weng, Y He
2019 ASABE Annual International Meeting, 1, 2019
Assessment of seed yield and quality of winter oilseed rape using chlorophyll fluorescence parameters of pods
H Xu, H Cen, Z Ma, L Wan, W Zhou, Y He
2018 ASABE Annual International Meeting, 1, 2018
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