Yan‐Fu Kuo

1000 total citations
58 papers, 719 citations indexed

About

Yan‐Fu Kuo is a scholar working on Plant Science, Analytical Chemistry and Molecular Biology. According to data from OpenAlex, Yan‐Fu Kuo has authored 58 papers receiving a total of 719 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Plant Science, 14 papers in Analytical Chemistry and 12 papers in Molecular Biology. Recurrent topics in Yan‐Fu Kuo's work include Smart Agriculture and AI (16 papers), Spectroscopy and Chemometric Analyses (14 papers) and Leaf Properties and Growth Measurement (9 papers). Yan‐Fu Kuo is often cited by papers focused on Smart Agriculture and AI (16 papers), Spectroscopy and Chemometric Analyses (14 papers) and Leaf Properties and Growth Measurement (9 papers). Yan‐Fu Kuo collaborates with scholars based in Taiwan and United States. Yan‐Fu Kuo's co-authors include Chia‐Lin Chung, Szu‐Yu Chen, Hao-Chun Hsu, Chun‐Neng Wang, Ta‐Te Lin, Cheng-Chun Wang, K.-P. Ho, Yu‐Jung Tsai, Chen-Yi Lin and Yao-Chuan Tsai and has published in prestigious journals such as SHILAP Revista de lepidopterología, Frontiers in Plant Science and Poultry Science.

In The Last Decade

Yan‐Fu Kuo

53 papers receiving 676 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yan‐Fu Kuo Taiwan	16	384	200	121	116	89	58	719
Boaz Zion Israel	18	287 0.7×	209 1.0×	405 3.3×	123 1.1×	63 0.7×	49	1.1k
Limiao Deng China	15	461 1.2×	320 1.6×	81 0.7×	68 0.6×	98 1.1×	36	808
R.D. Tillett United Kingdom	13	192 0.5×	53 0.3×	113 0.9×	44 0.4×	108 1.2×	40	605
Chunlei Xia China	17	365 1.0×	73 0.4×	87 0.7×	71 0.6×	152 1.7×	47	860
Ekrem Misimi Norway	18	88 0.2×	150 0.8×	239 2.0×	135 1.2×	33 0.4×	37	1.0k
Sierra Young United States	13	530 1.4×	143 0.7×	35 0.3×	39 0.3×	53 0.6×	39	786
Chengzhi Ruan China	13	326 0.8×	119 0.6×	37 0.3×	16 0.1×	81 0.9×	28	509
Md Sultan Mahmud United States	12	539 1.4×	127 0.6×	38 0.3×	31 0.3×	52 0.6×	29	751
Adair da Silva Oliveira Brazil	8	173 0.5×	41 0.2×	90 0.7×	29 0.3×	45 0.5×	12	373

Countries citing papers authored by Yan‐Fu Kuo

Since Specialization

Citations

This map shows the geographic impact of Yan‐Fu Kuo'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 Yan‐Fu Kuo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yan‐Fu Kuo more than expected).

Fields of papers citing papers by Yan‐Fu Kuo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yan‐Fu Kuo. 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 Yan‐Fu Kuo. The network helps show where Yan‐Fu Kuo may publish in the future.

Co-authorship network of co-authors of Yan‐Fu Kuo

This figure shows the co-authorship network connecting the top 25 collaborators of Yan‐Fu Kuo. A scholar is included among the top collaborators of Yan‐Fu Kuo 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 Yan‐Fu Kuo. Yan‐Fu Kuo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Chen, Bolin, et al.. (2025). Designing an Autonomous Robot for Monitoring Open-Mouth Behavior of Chickens in Commercial Chicken Farms. Journal of the ASABE. 68(1). 25–36. 1 indexed citations

Lin, Yun, et al.. (2024). Automated Identification of Tomato Pests, Diseases, and Disorders Using Convolutional Neural Networks. 2 indexed citations

Chen, Bolin, et al.. (2023). Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning. Poultry Science. 102(12). 103040–103040. 15 indexed citations

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

Hsu, Hao-Chun & Yan‐Fu Kuo. (2021). Nectar Guide Patterns on Developmentally Homologous Regions of the Subtribe Ligeriinae (Gesneriaceae). Frontiers in Plant Science. 12. 650836–650836. 2 indexed citations

Ho, K.-P., Yu‐Jung Tsai, & Yan‐Fu Kuo. (2021). Automatic monitoring of lactation frequency of sows and movement quantification of newborn piglets in farrowing houses using convolutional neural networks. Computers and Electronics in Agriculture. 189. 106376–106376. 26 indexed citations

Hsu, Hao-Chun, et al.. (2020). Automatic Identification of First-Order Veins and Corolla Contours in Three-Dimensional Floral Images. Frontiers in Plant Science. 11. 549699–549699. 1 indexed citations

Thai, Chi N., Yan‐Fu Kuo, & Ping‐Lang Yen. (2020). Experiences in Cross-Teaching within a Distance Education Environment. Papers on Engineering Education Repository (American Society for Engineering Education). 21.24.1–21.24.15.

Hsu, Hao-Chun, et al.. (2019). <i>Quantifying color and textural patterns of petals and studying their association with pollinators: using genus Sinningia (GESNERIACEAE) as an example</i>. 2019 Boston, Massachusetts July 7- July 10, 2019. 1 indexed citations

10.

Chen, Szu‐Yu, et al.. (2019). Counting bacterial colony on agar plates using deep convolutional neural network. 2019 Boston, Massachusetts July 7- July 10, 2019. 2 indexed citations

11.

Kuo, Yan‐Fu, et al.. (2019). Identifying species of common sea fish harvested by longliner using deep convolutional neural networks. 2019 Boston, Massachusetts July 7- July 10, 2019.

12.

Hsu, Hao-Chun, et al.. (2017). Association between Petal Form Variation and CYC2-like Genotype in a Hybrid Line of Sinningia speciosa. Frontiers in Plant Science. 8. 558–558. 16 indexed citations

13.

Kuo, Yan‐Fu, et al.. (2017). <i>Three Dimensional Phenotype Quantitative System of Seedling Root</i>. 2017 Spokane, Washington July 16 - July 19, 2017. 1 indexed citations

14.

Kuo, Yan‐Fu, et al.. (2015). Measurement of Residual Bran Distribution on Milled Rice Using Fluorescence Fingerprint-derived Imaging. Food Science and Technology Research. 21(2). 187–192. 1 indexed citations

15.

Wang, Chun‐Neng, et al.. (2015). Quantifying floral shape variation in 3D using microcomputed tomography: a case study of a hybrid line between actinomorphic and zygomorphic flowers. Frontiers in Plant Science. 6. 724–724. 35 indexed citations

16.

Hsu, Hao-Chun, et al.. (2015). Quantitative analysis of floral symmetry and tube dilation in an F2 cross of Sinningia speciosa. Scientia Horticulturae. 188. 71–77. 10 indexed citations

17.

Kuo, Yan‐Fu, et al.. (2015). QR code detection using convolutional neural networks. 1–5. 28 indexed citations

18.

Yeh, Yu-Ying, et al.. (2013). A comparison of machine learning methods on Hyperspectral plant disease assessments. 1 indexed citations

19.

Kuo, Yan‐Fu, et al.. (2012). Experimental Characterization of Transient Tone Deviation in Print Jobs for Color Electrophotography. Journal of Imaging Science and Technology. 56(2). 20502–1. 1 indexed citations

20.

Yih, Yuehwern, et al.. (2010). Improving Tone Prediction in Calibration of Electrophotographic Printers by Linear Regression: Environmental, Consumables and Tone-Level Factors. Journal of Imaging Science and Technology. 54(5). 50301–1. 3 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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