Helong Yu

798 total citations · 1 hit paper
31 papers, 525 citations indexed

About

Helong Yu is a scholar working on Plant Science, Analytical Chemistry and Molecular Biology. According to data from OpenAlex, Helong Yu has authored 31 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Plant Science, 8 papers in Analytical Chemistry and 5 papers in Molecular Biology. Recurrent topics in Helong Yu's work include Smart Agriculture and AI (23 papers), Spectroscopy and Chemometric Analyses (8 papers) and Plant Disease Management Techniques (6 papers). Helong Yu is often cited by papers focused on Smart Agriculture and AI (23 papers), Spectroscopy and Chemometric Analyses (8 papers) and Plant Disease Management Techniques (6 papers). Helong Yu collaborates with scholars based in China, Canada and Iran. Helong Yu's co-authors include Chengcheng Chen, Huiling Chen, Ali Asghar Heidari, Shuo Zhang, Nan Hu, Jiawen Liu, Zhennao Cai, Daoliang Li, Mingming Hao and Jian Zhang and has published in prestigious journals such as Scientific Reports, IEEE Access and Frontiers in Plant Science.

In The Last Decade

Helong Yu

27 papers receiving 501 citations

Hit Papers

Apple leaf disease recognition method with improved resid... 2022 2026 2023 2024 2022 25 50 75 100
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. Apple leaf disease recognition method with improved residual network
    2022 113 citations Helong Yu, Chengcheng Chen et al. Multimedia Tools and Applications profile →

Peers

Helong Yu
Aydın Kaya Türkiye
Anup Majumder Bangladesh
Jingdun Jia China
Diego Patrício Brazil
Ali Seydi Keçeli Türkiye
Deepak Sinwar India
Emine Uçar Türkiye
Zhihao Liu China
Aydın Kaya Türkiye
Helong Yu
Citations per year, relative to Helong Yu Helong Yu (= 1×) peers Aydın Kaya

Countries citing papers authored by Helong Yu

Since Specialization
Citations

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

Fields of papers citing papers by Helong Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Helong Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Helong Yu. A scholar is included among the top collaborators of Helong Yu 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 Helong Yu. Helong Yu 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.
Yu, Helong, et al.. (2025). J-Rice-ResNeXt: A deep learning-enhanced framework for high-accuracy Japonica rice varietal classification in precision agriculture. Industrial Crops and Products. 237. 122197–122197. 1 indexed citations
2.
Yu, Helong, et al.. (2025). Rapid and accurate classification of mung bean seeds based on HPMobileNet. Frontiers in Plant Science. 15. 1474906–1474906. 5 indexed citations
3.
Chen, Chengcheng, et al.. (2025). KD-SSGD: knowledge distillation-enhanced semi-supervised germination detection. Frontiers in Plant Science. 16. 1688792–1688792.
4.
Ullah, Aziz, et al.. (2025). AMS-YOLO: multi-scale feature integration for intelligent plant protection against maize pests. Frontiers in Plant Science. 16. 1640405–1640405.
5.
Li, Haiqing, et al.. (2025). Optimization of multi-pass coating for magnetic-thermal-assisted laser cladding based on data-enhanced WOA-DE-TELM and LHS-AMOPSO algorithm. Surface and Coatings Technology. 497. 131765–131765. 1 indexed citations
6.
Bi, Xinhua, Ziyi Song, Jinge Li, et al.. (2025). DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion. Frontiers in Plant Science. 16. 1588901–1588901.
7.
Yu, Helong, et al.. (2024). Improved XLNet modeling for Chinese named entity recognition of edible fungus. Frontiers in Plant Science. 15. 1368847–1368847.
8.
Zhou, Lei, et al.. (2024). Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming. Frontiers in Plant Science. 15. 1346182–1346182. 3 indexed citations
9.
Yu, Helong, et al.. (2023). Research on weed identification in soybean fields based on the lightweight segmentation model DCSAnet. Frontiers in Plant Science. 14. 1268218–1268218. 5 indexed citations
10.
Hu, Nan, et al.. (2023). Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model. Agronomy. 13(2). 300–300. 54 indexed citations
11.
Ma, Wei, et al.. (2023). Crop Disease Detection against Complex Background Based on Improved Atrous Spatial Pyramid Pooling. Electronics. 12(1). 216–216. 9 indexed citations
12.
Yu, Helong, et al.. (2023). An intent classification method for questions in "Treatise on Febrile diseases" based on TinyBERT-CNN fusion model. Computers in Biology and Medicine. 162. 107075–107075. 13 indexed citations
13.
Ma, Li, et al.. (2023). Maize Leaf Disease Identification Based on YOLOv5n Algorithm Incorporating Attention Mechanism. Agronomy. 13(2). 521–521. 30 indexed citations
14.
Zhang, Zhe, et al.. (2023). A novel method for Pu-erh tea face traceability identification based on improved MobileNetV3 and triplet loss. Scientific Reports. 13(1). 6986–6986. 2 indexed citations
15.
Yu, Helong, et al.. (2022). Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism. Frontiers in Plant Science. 13. 890051–890051. 19 indexed citations
16.
Yu, Helong, et al.. (2022). Development of Weed Detection Method in Soybean Fields Utilizing Improved DeepLabv3+ Platform. Agronomy. 12(11). 2889–2889. 27 indexed citations
17.
Yu, Helong, Chengcheng Chen, Ali Asghar Heidari, et al.. (2022). Apple leaf disease recognition method with improved residual network. Multimedia Tools and Applications. 81(6). 7759–7782. 113 indexed citations breakdown →
18.
Li, Hongda, et al.. (2021). Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network. Applied Engineering in Agriculture. 37(5). 929–940. 3 indexed citations
19.
Han, Ye, Fei He, Yongbing Chen, Yuanning Liu, & Helong Yu. (2018). SiRNA silencing efficacy prediction based on a deep architecture. BMC Genomics. 19(S7). 669–669. 20 indexed citations
20.
Zhu, Yungang, Dayou Liu, Guifen Chen, Haiyang Jia, & Helong Yu. (2011). Mathematical modeling for active and dynamic diagnosis of crop diseases based on Bayesian networks and incremental learning. Mathematical and Computer Modelling. 58(3-4). 514–523. 17 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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