Young Chang

1.6k total citations · 1 hit paper
51 papers, 1.1k citations indexed

About

Young Chang is a scholar working on Plant Science, Cell Biology and Analytical Chemistry. According to data from OpenAlex, Young Chang has authored 51 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Plant Science, 14 papers in Cell Biology and 8 papers in Analytical Chemistry. Recurrent topics in Young Chang's work include Berry genetics and cultivation research (17 papers), Smart Agriculture and AI (17 papers) and Plant Pathogens and Fungal Diseases (14 papers). Young Chang is often cited by papers focused on Berry genetics and cultivation research (17 papers), Smart Agriculture and AI (17 papers) and Plant Pathogens and Fungal Diseases (14 papers). Young Chang collaborates with scholars based in Canada, United States and Norway. Young Chang's co-authors include Jaemyung Shin, Qamar U. Zaman, Tanzeel U. Rehman, Md Sultan Mahmud, Arnold W. Schumann, Jian Jin, Travis J. Esau, Aitazaz A. Farooque, Brandon Heung and G.W. Price and has published in prestigious journals such as Journal of Food Engineering, Journal of Animal Science and LWT.

In The Last Decade

Young Chang

46 papers receiving 1.0k citations

Hit Papers

Current and future applications of statistical machine le... 2018 2026 2020 2023 2018 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems
    2018 294 citations Tanzeel U. Rehman, Md Sultan Mahmud et al. Computers and Electronics in Agriculture profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Young Chang Canada 18 768 240 220 96 89 51 1.1k
Chunjiang Zhao China 22 1.0k 1.3× 251 1.0× 248 1.1× 124 1.3× 59 0.7× 85 1.4k
Qamar U. Zaman Canada 21 864 1.1× 178 0.7× 294 1.3× 195 2.0× 87 1.0× 91 1.2k
Long He United States 19 761 1.0× 122 0.5× 143 0.7× 158 1.6× 50 0.6× 68 996
Md Sultan Mahmud United States 12 539 0.7× 127 0.5× 145 0.7× 99 1.0× 34 0.4× 29 751
John K. Schueller United States 21 726 0.9× 192 0.8× 211 1.0× 188 2.0× 33 0.4× 111 1.3k
Matthew D. Whiting United States 27 1.9k 2.5× 120 0.5× 164 0.7× 130 1.4× 69 0.8× 121 2.2k
Cheryl McCarthy Australia 12 979 1.3× 255 1.1× 282 1.3× 188 2.0× 37 0.4× 31 1.2k
Mads Dyrmann Denmark 15 969 1.3× 254 1.1× 422 1.9× 126 1.3× 33 0.4× 32 1.2k
Masayuki Hirafuji Japan 17 788 1.0× 167 0.7× 377 1.7× 205 2.1× 39 0.4× 84 1.3k
Huanyu Jiang China 19 1.1k 1.4× 360 1.5× 261 1.2× 152 1.6× 48 0.5× 85 1.5k

Countries citing papers authored by Young Chang

Since Specialization
Citations

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

Fields of papers citing papers by Young Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young Chang. 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 Young Chang. The network helps show where Young Chang may publish in the future.

Co-authorship network of co-authors of Young Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Young Chang. A scholar is included among the top collaborators of Young Chang 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 Young Chang. Young Chang 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.
Chang, Young, et al.. (2025). Real-Time agricultural image encryption algorithm using AES on edge computing devices. Computers and Electronics in Agriculture. 237. 110594–110594. 3 indexed citations
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Chang, Young, et al.. (2024). Application of a Real-Time Field-Programmable Gate Array-Based Image-Processing System for Crop Monitoring in Precision Agriculture. AgriEngineering. 6(3). 3345–3361. 1 indexed citations
5.
Ikram, Muhammad Z., et al.. (2024). Flexible temperature and humidity sensors of plants for precision agriculture: Current challenges and future roadmap. Computers and Electronics in Agriculture. 226. 109449–109449. 18 indexed citations
6.
Chang, Young, et al.. (2024). Cyber security in smart agriculture: Threat types, current status, and future trends. Computers and Electronics in Agriculture. 226. 109401–109401. 14 indexed citations
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Shin, Jaemyung, et al.. (2022). Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture. AgriEngineering. 5(1). 20–39. 34 indexed citations
9.
Ravichandran, Sridhar, et al.. (2022). Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning. Cereal Chemistry. 99(4). 907–919. 10 indexed citations
10.
Zaman, Qamar U., et al.. (2021). An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields. Artificial Intelligence in Agriculture. 5. 72–81. 4 indexed citations
11.
Shin, Jaemyung, Young Chang, Brandon Heung, et al.. (2021). A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Computers and Electronics in Agriculture. 183. 106042–106042. 127 indexed citations
12.
Mahmud, Md Sultan, Qamar U. Zaman, Travis J. Esau, et al.. (2020). Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System. Agronomy. 10(7). 1027–1027. 24 indexed citations
13.
Shin, Jaemyung, et al.. (2019). Optimizing parameters for image processing techniques using machine learning to detect powdery mildew in strawberry leaves. 2019 Boston, Massachusetts July 7- July 10, 2019. 3 indexed citations
14.
Rehman, Tanzeel U., Qamar U. Zaman, Young Chang, Arnold W. Schumann, & Kenneth Corscadden. (2019). Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry. Computers and Electronics in Agriculture. 162. 1–13. 34 indexed citations
15.
Esau, Travis J., Qamar U. Zaman, Dominic Groulx, et al.. (2017). Machine vision for spot-application of agrochemical in wild blueberry fields. Advances in Animal Biosciences. 8(2). 272–276. 2 indexed citations
16.
Chang, Young, et al.. (2017). A real-time ultrasonic system to measure wild blueberry plant height during harvesting. Biosystems Engineering. 157. 35–44. 25 indexed citations
17.
Farooque, Aitazaz A., Qamar U. Zaman, Tri Nguyen-Quang, et al.. (2016). Development of a Predictive Model for Wild Blueberry Harvester Fruit Losses during Harvesting Using Artificial Neural Network. Applied Engineering in Agriculture. 32(6). 725–738. 5 indexed citations
18.
Zaman, Qamar U., et al.. (2014). Impact of Variable Rate Fertilization on Nutrients Losses in Surface Runofffor Wild Blueberry Fields. Applied Engineering in Agriculture. 179–185. 1 indexed citations
19.
Farooque, Aitazaz A., Young Chang, Qamar U. Zaman, et al.. (2013). Performance evaluation of multiple ground based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time. Computers and Electronics in Agriculture. 91. 135–144. 42 indexed citations
20.
Chang, Young, Qamar U. Zaman, Aitazaz A. Farooque, Arnold W. Schumann, & David Percival. (2011). An automated yield monitoring system II for commercial wild blueberry double-head harvester. Computers and Electronics in Agriculture. 81. 97–103. 24 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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