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About the database ScholarIQanswers from OpenAlex & ORCID
How has Shih‐Fang Chen's publication output changed over time?
ScholarIQpublication output · 2002–2022
Output grew0% over the shown period — from 1 works in 2002 to 1 in 2022.
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2002200320042011201420152017202020212022
What are the most-cited papers on Shih‐Fang Chen?
ScholarIQmost cited works
On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system
Min-Sheng Liao, Shih‐Fang Chen, Cheng‐Ying Chou, Hsun‐Yi Chen, Shih-Hao Yeh, Yu-Chi Chang, Joe‐Air Jiang
Computers and Electronics in Agriculture. 2017123 Citations
Localizing plucking points of tea leaves using deep convolutional neural networks
Yu‐Ting Chen, Shih‐Fang Chen
Computers and Electronics in Agriculture. 2020100 Citations
Influence of the hole injection layer on the luminescent performanceof organic light-emitting diodes
Shih‐Fang Chen, Ching‐Wu Wang
S105243760. 200490 Citations
The hetero-epitaxial SiCN/Si MSM photodetector for high-temperature deep-UV detecting applications
Wen-Rong Chang, Yean-Kuen Fang, S.F. Ting, Yong-Shiuan Tsair, Cheng-Nan Chang, Chun‐Yu Lin, Shih‐Fang Chen
S19887683. 200378 Citations
Prediction of specialty coffee flavors based on near‐infrared spectra using machine‑ and deep‐learning methods
Yu‐Tang Chang, Meng‐Chien Hsueh, Shu‐Pin Hung, Juin‐Ming Lu, Jia‐Hung Peng, Shih‐Fang Chen
S206298197. 202172 Citations
Related on ScholarIQ
On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system
Paper
Localizing plucking points of tea leaves using deep convolutional neural networks
Paper
Influence of the hole injection layer on the luminescent performanceof organic light-emitting diodes
Paper
The hetero-epitaxial SiCN/Si MSM photodetector for high-temperature deep-UV detecting applications
Paper
Prediction of specialty coffee flavors based on near‐infrared spectra using machine‑ and deep‐learning methods
Paper
Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network
Paper