Utpal Barman

801 total citations · 1 hit paper
29 papers, 481 citations indexed

About

Utpal Barman is a scholar working on Plant Science, Analytical Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, Utpal Barman has authored 29 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Plant Science, 9 papers in Analytical Chemistry and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Utpal Barman's work include Smart Agriculture and AI (17 papers), Spectroscopy and Chemometric Analyses (9 papers) and Plant Disease Management Techniques (4 papers). Utpal Barman is often cited by papers focused on Smart Agriculture and AI (17 papers), Spectroscopy and Chemometric Analyses (9 papers) and Plant Disease Management Techniques (4 papers). Utpal Barman collaborates with scholars based in India, United States and Ghana. Utpal Barman's co-authors include Ridip Dev Choudhury, Manob Jyoti Saikia, Parismita Sarma, Mirzanur Rahman, Vaishali Sharma, Nirmal Mazumder, N. K. Rahman, Mala Dutta, Anirban Bhattacharjee and Pranveer Singh and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computers and Electronics in Agriculture and Energies.

In The Last Decade

Utpal Barman

26 papers receiving 446 citations

Hit Papers

ViT-SmartAgri: Vision Transformer and Smartphone-Based Pl... 2024 2026 2025 2024 10 20 30 40
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. ViT-SmartAgri: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture
    2024 49 citations Utpal Barman, Parismita Sarma et al. Agronomy profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Utpal Barman India 10 337 138 66 63 34 29 481
Ridip Dev Choudhury India 7 235 0.7× 91 0.7× 45 0.7× 60 1.0× 29 0.9× 16 340
Zhihao Liu China 7 311 0.9× 91 0.7× 62 0.9× 30 0.5× 24 0.7× 15 474
Anil Bhujel South Korea 12 263 0.8× 57 0.4× 52 0.8× 35 0.6× 76 2.2× 20 481
Marcelo Theophilo Folhes Brazil 6 466 1.4× 122 0.9× 202 3.1× 60 1.0× 32 0.9× 8 606
Narendra Singh Chandel India 14 484 1.4× 121 0.9× 172 2.6× 43 0.7× 14 0.4× 41 645
Sun Zhong-fu China 9 608 1.8× 227 1.6× 159 2.4× 42 0.7× 12 0.4× 21 711
Xiang Yin China 14 270 0.8× 218 1.6× 42 0.6× 20 0.3× 26 0.8× 45 526
Keming Du China 10 669 2.0× 270 2.0× 188 2.8× 50 0.8× 12 0.4× 15 772
Evans Asenso Ghana 10 247 0.7× 68 0.5× 37 0.6× 16 0.3× 66 1.9× 27 483
Daeun Choi United States 15 478 1.4× 73 0.5× 91 1.4× 110 1.7× 9 0.3× 43 623

Countries citing papers authored by Utpal Barman

Since Specialization
Citations

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

Fields of papers citing papers by Utpal Barman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Utpal Barman

This figure shows the co-authorship network connecting the top 25 collaborators of Utpal Barman. A scholar is included among the top collaborators of Utpal Barman 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 Utpal Barman. Utpal Barman 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.
Barman, Utpal, et al.. (2025). 15 Years of optimizers in medical deep learning: A systematic review. SHILAP Revista de lepidopterología. 6(1). 100249–100249.
2.
Barman, Utpal, et al.. (2025). Advancing deep learning for automated stroke detection: a review. SHILAP Revista de lepidopterología. 6(5). 247–260. 1 indexed citations
3.
4.
Barman, Utpal, et al.. (2024). ViT-SmartAgri: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture. Agronomy. 14(2). 327–327. 49 indexed citations breakdown →
5.
Barman, Utpal, et al.. (2024). Deep Neural Network-Based Smart Grid Stability Analysis: Enhancing Grid Resilience and Performance. Energies. 17(11). 2642–2642. 6 indexed citations
6.
7.
Barman, Utpal, et al.. (2024). Static analysis framework for permission-based dataset generation and android malware detection using machine learning. SHILAP Revista de lepidopterología. 2024(1). 4 indexed citations
8.
Barman, Utpal, et al.. (2024). Innovative Approaches to Rice (Oryza sativa) Crop Health: A Comprehensive Analysis of Deep Transfer Learning for Early Disease Detection. Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi. 34(2). 314–322. 4 indexed citations
9.
Barman, Utpal, et al.. (2023). Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis. Multimedia Tools and Applications. 82(16). 25083–25105. 11 indexed citations
10.
Barman, Utpal, et al.. (2023). Performance analysis of support vector machine for early identification of citrus diseases. Journal of Applied and Natural Science. 15(2). 852–859. 4 indexed citations
11.
Singh, Pranveer, et al.. (2023). A discussion to promote global sustainability techniques for motivating plastic waste to minimize carbon footprints. Environmental Quality Management. 33(1). 327–339.
12.
Barman, Utpal, et al.. (2022). Smartphone assisting convolutional neural networks for soil texture classification in dry and wet humid conditions in West Guwahati, Assam. Journal of Applied and Natural Science. 14(4). 1351–1359. 2 indexed citations
13.
14.
Barman, Utpal, et al.. (2021). Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll. Journal of Applied and Natural Science. 13(4). 1249–1255. 1 indexed citations
15.
16.
Barman, Utpal, et al.. (2019). Predication of Soil pH using K mean Segmentation and HSV Color Image Processing. International Conference on Computing for Sustainable Global Development. 31–36. 6 indexed citations
17.
Barman, Utpal & Ridip Dev Choudhury. (2019). Bacterial and Virus affected Citrus Leaf Disease Classification using Smartphone and SVM. International Journal of Recent Technology and Engineering (IJRTE). 9(2). 4220–4226. 5 indexed citations
18.
Barman, Utpal. (2019). Prediction of Soil pH using Smartphone based Digital Image Processing and Prediction Algorithm. JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES. 14(2). 10 indexed citations
19.
Barman, Utpal & Ridip Dev Choudhury. (2019). Soil texture classification using multi class support vector machine. Information Processing in Agriculture. 7(2). 318–332. 108 indexed citations
20.
Barman, Utpal, et al.. (2018). Predication of soil pH using HSI colour image processing and regression over Guwahati, Assam, India. Journal of Applied and Natural Science. 10(2). 805–809. 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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