Shih‐Fang Chen

1.0k total citations
68 papers, 801 citations indexed

About

Shih‐Fang Chen is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Plant Science. According to data from OpenAlex, Shih‐Fang Chen has authored 68 papers receiving a total of 801 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Electrical and Electronic Engineering, 16 papers in Biomedical Engineering and 13 papers in Plant Science. Recurrent topics in Shih‐Fang Chen's work include Smart Agriculture and AI (12 papers), Advanced Electrical Measurement Techniques (10 papers) and Advanced Chemical Sensor Technologies (8 papers). Shih‐Fang Chen is often cited by papers focused on Smart Agriculture and AI (12 papers), Advanced Electrical Measurement Techniques (10 papers) and Advanced Chemical Sensor Technologies (8 papers). Shih‐Fang Chen collaborates with scholars based in Taiwan, United States and China. Shih‐Fang Chen's co-authors include Ching‐Wu Wang, Yu‐Ting Chen, Mary‐Grace C. Danao, Yean-Kuen Fang, S.F. Ting, Joe‐Air Jiang, Cheng‐Ying Chou, Yu-Kai Lin, Hsun‐Yi Chen and Patrick J. Brown and has published in prestigious journals such as Applied Physics Letters, Journal of Applied Physics and Journal of The Electrochemical Society.

In The Last Decade

Shih‐Fang Chen

66 papers receiving 769 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shih‐Fang Chen Taiwan 14 368 230 181 155 102 68 801
J. De Baerdemaeker Belgium 14 126 0.3× 262 1.1× 191 1.1× 223 1.4× 262 2.6× 108 1.0k
Bronson Philippa Australia 16 454 1.2× 311 1.4× 138 0.8× 91 0.6× 64 0.6× 51 1.0k
Shiping Zhu China 15 370 1.0× 70 0.3× 184 1.0× 232 1.5× 146 1.4× 40 688
Hongjian Chen China 20 278 0.8× 80 0.3× 198 1.1× 235 1.5× 20 0.2× 76 912
Taek Sung Lee South Korea 20 564 1.5× 118 0.5× 412 2.3× 336 2.2× 23 0.2× 62 1.1k
Deepak Bhatnagar India 19 631 1.7× 370 1.6× 176 1.0× 136 0.9× 87 0.9× 129 1.3k
Zoran Stamenković Germany 13 664 1.8× 121 0.5× 156 0.9× 297 1.9× 24 0.2× 93 1.0k
Szu‐Yu Chen Taiwan 15 172 0.5× 245 1.1× 118 0.7× 172 1.1× 132 1.3× 28 749
Jinyang Li China 18 117 0.3× 170 0.7× 178 1.0× 196 1.3× 12 0.1× 74 1.1k
Haris Ahmad Khan Netherlands 14 121 0.3× 113 0.5× 59 0.3× 99 0.6× 207 2.0× 36 616

Countries citing papers authored by Shih‐Fang Chen

Since Specialization
Citations

This map shows the geographic impact of Shih‐Fang Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shih‐Fang Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shih‐Fang Chen more than expected).

Fields of papers citing papers by Shih‐Fang Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shih‐Fang Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shih‐Fang Chen. The network helps show where Shih‐Fang Chen may publish in the future.

Co-authorship network of co-authors of Shih‐Fang Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Shih‐Fang Chen. A scholar is included among the top collaborators of Shih‐Fang Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shih‐Fang Chen. Shih‐Fang Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lin, Yun, et al.. (2022). Identifying tomato leaf diseases under real field conditions using convolutional neural networks and a chatbot. Computers and Electronics in Agriculture. 202. 107365–107365. 13 indexed citations
2.
Lee, Shih‐Yu, et al.. (2022). Developing a Self-Guided Field Robot for Greenhouse Asparagus Monitoring. 1 indexed citations
3.
Chen, Shih‐Fang, et al.. (2021). Prediction of specialty coffee flavors based on near‐infrared spectra using machine‑ and deep‐learning methods. Journal of the Science of Food and Agriculture. 101(11). 4705–4714. 48 indexed citations
5.
Lin, Yu-Kai, et al.. (2021). Developing a guiding and growth status monitoring system for riding-type tea plucking machine using fully convolutional networks. Computers and Electronics in Agriculture. 191. 106540–106540. 11 indexed citations
6.
Chen, Shih‐Fang, et al.. (2020). Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network. Plant Pathology. 69(9). 1731–1739. 32 indexed citations
7.
Maruyama, Michitaka, Shih‐Fang Chen, Mun-Seog Kim, & Kyu‐Tae Kim. (2019). APMP key comparison of DC voltage at 10 V and 1.018 V. Metrologia. 56(1A). 1009–1009. 1 indexed citations
8.
Lin, Yu-Kai & Shih‐Fang Chen. (2019). Development of Navigation System for Tea Field Machine Using Semantic Segmentation. IFAC-PapersOnLine. 52(30). 108–113. 22 indexed citations
9.
Chen, Yuting, Chia‐Chang Wu, & Shih‐Fang Chen. (2018). <i>Application of Deep Learning Algorithm on Tea Shoot Identification and Localization </i>. 2018 Detroit, Michigan July 29 - August 1, 2018. 4 indexed citations
11.
Maruyama, Michitaka, et al.. (2014). Generation of 10 Vrms waveforms using AC-programmable Josephson voltage standard system with 10 K cooler. 416–417. 6 indexed citations
12.
Chen, Shih‐Fang, Patrick J. Brown, & Mary‐Grace C. Danao. (2013). Stalk Strength and Sugar Content of Different Dual-Purpose Sorghum Lines. 2013 Kansas City, Missouri, July 21 - July 24, 2013. 1 indexed citations
13.
Yang, Haw‐Ching, et al.. (2011). Automatic feature selection and failure diagnosis for bearing faults. Society of Instrument and Control Engineers of Japan. 235–239. 1 indexed citations
14.
Tsai, Cheng‐Che, et al.. (2011). The characteristics of ultrasonic therapeutic transducers and used lead-free non-stoichiometric NKN-based piezoelectric ceramics. Current Applied Physics. 11(3). S128–S133. 3 indexed citations
15.
Chen, Shih‐Fang, et al.. (2007). Significantly Improved Luminance of Organic Light-Emitting Diodes by Doping Iodine and Nitrogen Treatment. Japanese Journal of Applied Physics. 46(4S). 2753–2753. 1 indexed citations
16.
Lin, Chun‐Yu, et al.. (2007). A novel electrochromic device with high optical switching speed. Journal of Physics and Chemistry of Solids. 69(2-3). 734–737. 8 indexed citations
17.
Chen, Shih‐Fang, et al.. (2007). The Effect of H-Treatment on Au-Induced Lateral Crystallization of Phosphorus-Doped a-Si:H Films. Electrochemical and Solid-State Letters. 10(8). H221–H221. 2 indexed citations
18.
Li, Jianfeng, Shih‐Fang Chen, Shui‐Hsiang Su, Kao‐Shing Hwang, & Meiso Yokoyama. (2006). Effects of hole injection layer thickness on the luminescent properties of white organic light-emitting diodes. Applied Surface Science. 253(5). 2522–2524. 8 indexed citations
19.
Lin, Chun‐Yu, et al.. (2006). Design and fabrication of a TiO 2 /nano-silicon composite visible light photocatalyst. Applied Surface Science. 253(2). 898–903. 19 indexed citations
20.
Chen, Shih‐Fang, et al.. (2002). Gamma-ray-irradiation effects on the leakage current and reliability of sputtered TiO2 gate oxide in metal–oxide–semiconductor capacitors. Journal of Applied Physics. 91(11). 9198–9203. 31 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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