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About the database ScholarIQanswers from OpenAlex & ORCID
How has Xiuhua Li's publication output changed over time?
ScholarIQpublication output · 2002–2024
Output grew0% over the shown period — from 1 works in 2002 to 1 in 2024.
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2002201020122014202220232024
What are the most-cited papers on Xiuhua Li?
ScholarIQmost cited works
Key technologies of machine vision for weeding robots: A review and benchmark
Yong Li, Zhiqiang Guo, Feng Shuang, Man Zhang, Xiuhua Li
Computers and Electronics in Agriculture. 2022128 Citations
Spectral difference analysis and airborne imaging classification for citrus greening infected trees
Xiuhua Li, Won Suk Lee, Minzan Li, Reza Ehsani, Ashish Mishra, Chenghai Yang, Robert L. Mangan
Computers and Electronics in Agriculture. 201296 Citations
Toxicity of Per- and Polyfluoroalkyl Substances to Aquatic Invertebrates, Planktons, and Microorganisms
Tingting Ma, Chaoran Ye, Tiantian Wang, Xiuhua Li, Yongming Luo
International Journal of Environmental Research and Public Health. 202283 CitationsOPEN ACCESS
Potential for biodegradation of polychlorinated biphenyls (PCBs) by Sinorhizobium meliloti
Chen Tu, Ying Teng, Yongming Luo, Xiuhua Li, Xianghui Sun, Zhengao Li, Wuxing Liu, Peter Christie
Journal of Hazardous Materials. 201077 Citations
SLViT: Shuffle-convolution-based lightweight Vision transformer for effective diagnosis of sugarcane leaf diseases
Xuechen Li, Xuechen Li, Xiuhua Li, Xiuhua Li, Shimin Zhang, Guiying Zhang, Muqing Zhang, Heyang Shang
S2764955546. 202273 CitationsOPEN ACCESS
Related on ScholarIQ
Guangxi University
Institution
Key technologies of machine vision for weeding robots: A review and benchmark
Paper
Spectral difference analysis and airborne imaging classification for citrus greening infected trees
Paper
Toxicity of Per- and Polyfluoroalkyl Substances to Aquatic Invertebrates, Planktons, and Microorganisms
Paper
Potential for biodegradation of polychlorinated biphenyls (PCBs) by Sinorhizobium meliloti
Paper
SLViT: Shuffle-convolution-based lightweight Vision transformer for effective diagnosis of sugarcane leaf diseases
Paper