Di Bai

904 total citations · 1 hit paper
40 papers, 597 citations indexed

About

Di Bai is a scholar working on Electrical and Electronic Engineering, Plant Science and Computer Vision and Pattern Recognition. According to data from OpenAlex, Di Bai has authored 40 papers receiving a total of 597 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Electrical and Electronic Engineering, 9 papers in Plant Science and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Di Bai's work include Video Surveillance and Tracking Methods (7 papers), Fire Detection and Safety Systems (7 papers) and Fire effects on ecosystems (7 papers). Di Bai is often cited by papers focused on Video Surveillance and Tracking Methods (7 papers), Fire Detection and Safety Systems (7 papers) and Fire effects on ecosystems (7 papers). Di Bai collaborates with scholars based in China, Canada and Australia. Di Bai's co-authors include Haifeng Lin, Renjie Xu, Kunquan Li, Xiaohua Wang, Ji Lin, Jingru Liu, Hua Li, Qilin Xue, Jingjing Qian and Chen Gong and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and Scientific Reports.

In The Last Decade

Di Bai

35 papers receiving 581 citations

Hit Papers

align trajectories

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.

YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5

2023 110 citations Renjie Xu, Di Bai et al. Forests profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Di Bai China	13	180	133	99	75	71	40	597
Yue Xi China	15	218 1.2×	151 1.1×	12 0.1×	62 0.8×	19 0.3×	48	782
Mahady Hasan Bangladesh	11	85 0.5×	51 0.4×	21 0.2×	10 0.1×	28 0.4×	104	597
Huihui Yu China	16	260 1.4×	96 0.7×	13 0.1×	10 0.1×	38 0.5×	24	867
Mario Nieto-Hidalgo Spain	9	196 1.1×	114 0.9×	11 0.1×	46 0.6×	113 1.6×	11	606
Anis Salwa Mohd Khairuddin Malaysia	17	112 0.6×	238 1.8×	24 0.2×	15 0.2×	22 0.3×	87	963
Uferah Shafi Pakistan	15	554 3.1×	63 0.5×	21 0.2×	8 0.1×	129 1.8×	18	1.2k
Narendra Khatri India	16	141 0.8×	26 0.2×	7 0.1×	64 0.9×	82 1.2×	46	751
Liu Fen China	15	85 0.5×	38 0.3×	19 0.2×	116 1.5×	50 0.7×	38	727
Yingxin Ye China	12	105 0.6×	38 0.3×	88 0.9×	22 0.3×	23 0.3×	21	1.1k

Countries citing papers authored by Di Bai

Since Specialization

Citations

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

Fields of papers citing papers by Di Bai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Di Bai

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Chen, Yijia, et al.. (2025). Efficient face information encryption and verification scheme based on full homomorphic encryption. Scientific Reports. 15(1). 11388–11388. 1 indexed citations

2.

Chen, Zhichao, Haifeng Lin, Di Bai, et al.. (2025). PWDViTNet: A lightweight early pine wilt disease detection model based on the fusion of ViT and CNN. Computers and Electronics in Agriculture. 230. 109910–109910. 6 indexed citations

3.

Bai, Di, Shuo Ma, Xiaochen Yang, et al.. (2024). A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples. Energy and Buildings. 325. 115021–115021. 2 indexed citations

4.

Lin, Haifeng, et al.. (2024). A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning. Forests. 15(4). 591–591. 6 indexed citations

5.

Chen, Zhichao, Hongping Zhou, Haifeng Lin, & Di Bai. (2024). TeaViTNet: Tea Disease and Pest Detection Model Based on Fused Multiscale Attention. Agronomy. 14(3). 633–633. 13 indexed citations

6.

Bai, Di, et al.. (2023). Research on MPPT control strategy based on the Perturbation observation method. Journal of Physics Conference Series. 2474(1). 12073–12073. 1 indexed citations

7.

Wang, Yuhao, et al.. (2023). An Enhanced Measurement for Inorganics in Water Based on a Novel Planar Three-Electrode Sensor. IEEE Sensors Journal. 23(13). 14988–14996. 1 indexed citations

8.

Li, Ran & Di Bai. (2023). Electric vehicle charging scheduling based on improved bald eagle algorithm. Journal of Physics Conference Series. 2656(1). 12024–12024. 1 indexed citations

9.

Bai, Di, et al.. (2023). FlameTransNet: Advancing Forest Flame Segmentation with Fusion and Augmentation Techniques. Forests. 14(9). 1887–1887. 11 indexed citations

10.

Bai, Di, Wanguo Jiao, Ling Jiang, et al.. (2023). A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets. Forests. 14(10). 2089–2089. 4 indexed citations

11.

Gong, Chen, Hang Zhou, Zhongyuan Li, et al.. (2023). Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s. Forests. 14(2). 315–315. 29 indexed citations

12.

Bai, Di, et al.. (2023). Optimal scheduling of integrated energy systems with carbon and hydrogen storage based on IGDT. Journal of Physics Conference Series. 2563(1). 12024–12024.

13.

Xu, Renjie, et al.. (2023). Integrated Learning-Based Pest and Disease Detection Method for Tea Leaves. Forests. 14(5). 1012–1012. 22 indexed citations

14.

Xu, Renjie, et al.. (2023). YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5. Forests. 14(2). 415–415. 110 indexed citations breakdown →

15.

Gong, Chen, et al.. (2023). LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7. Remote Sensing. 15(15). 3790–3790. 30 indexed citations

16.

Qian, Jingjing, Ji Lin, Di Bai, Renjie Xu, & Haifeng Lin. (2023). Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model. Forests. 14(4). 838–838. 29 indexed citations

17.

Lin, Haifeng, Xinyi Li, & Di Bai. (2020). Assembly and phylogenetic analysis of the complete chloroplast genome of Citrus aurantium (Rutaceae). SHILAP Revista de lepidopterología. 5(3). 2250–2251. 4 indexed citations

18.

Bai, Di, et al.. (2017). Research on grid-connected micro grid control strategy and control method. 1–4. 1 indexed citations

19.

Bai, Di, et al.. (2016). Security Enhancement of Health Information Exchange Based on Cloud Computing System. International Journal of Scientific Engineering and Research. 4(10). 79–82. 12 indexed citations

20.

Bai, Di, et al.. (2016). Research and simulation of V2G technology in micro grid. 1–5. 2 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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