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Pages
Jinze Huang 黄金泽
About me
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Posts
Future Blog Post
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Blog Post number 1
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portfolio
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publications
DiseSniper: A potato disease identification system based on the ResNet model.
Published in 2022 10th International Conference on Agro-geoinformatics (Agro-Geoinformatics), 2022
Potato is one of China most promising high-production cash crops. However, the frequent occurrence of potato diseases and insect pests directly leads to a considerable crop reduction or low yield, restricting the agricultural economy development.
Recommended citation: Y. Wang et al., "DiseSniper: A potato disease identification system based on the ResNet model," 2022 10th International Conference on Agro-geoinformatics (Agro-Geoinformatics), 2022, pp. 1-4, doi: 10.1109/Agro-Geoinformatics55649.2022.9859214. http://RogerHuangPKX.github.io/files/paper3-1.pdf
Comparison of Inversion Methods for Maize Canopy Time-series LAI Based on SupReME Reconstructed Images.
Published in Journal of the ASABE, 2022
Accurate time-series crop leaf area index (LAI) monitoring can provide data support for field management and early yield estimation. The Sentinel-2 satellite has a high spatial, temporal, and spectral resolution, and its unique three red-edge bands provide an ideal data source for LAI estimation. However, the inconsistent spatial resolution of different bands hinders the application potential of Sentinel-2 images. In view of this problem, we focused on mining more infor- mation provided by the high spatial resolution bands of Sentinel-2 images using the Super-Resolution for Multispectral Multiresolution Estimation (SupReME) algorithm. Furthermore, The SNAP (Sentinel Application Platform) biophysical processor and the PROSAIL radiation transfer model coupled with Random Forest (RF) model were applied to estimate time-series LAI of maize canopy at 10 m spatial resolution, and the Leaf Area Index Wireless Sensor Network (LAINet) measurements were used for accuracy verification. Finally, the effectiveness of images reconstructed by SupReME and the two inversion methods for time-series LAI estimation were evaluated. The results showed that: (1) the Sentinel-2 images reconstructed by SupReME can improve spatial characteristics while maintaining spectral invariance, and they were more advantageous for LAI estimation than the original images; (2) The SNAP biophysical processor suits a quick large-scale estimation with robustness, while the PROSAIL coupled RF model achieved a higher coefficient of determination (R2) and a lower root mean square error (RMSE) (R2 increased by more than 0.1, RMSE decreased by more than 0.33) for time- series LAI estimation in this specific study area; (3) both inversion methods showed apparent underestimation at the late growth stage. This study verifies the feasibility of obtaining high spatial resolution images using a super-resolution algo- rithm for LAI inversion and provides the effect of two commonly used inversion methods for time-series LAI estimation at 10 m resolution.
Recommended citation: Y. Li et al., “Comparison of Inversion Methods for Maize Canopy Time-Series LAI Based on SupReME Reconstructed Images,” J. Agric. Saf. Health, vol. 65, no. 5, pp. 1019–1028, 2022, doi: 10.13031/ja.15011. http://RogerHuangPKX.github.io/files/paper-1.pdf
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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