Tanuja Sheorey

1.0k citations
34 papers · 596 indexed · 3 hit papers · h-index 12
Topics
Smart Agriculture and AI (6 papers)Heat Transfer Mechanisms (5 papers)Manufacturing Process and Optimization (4 papers)
Partner nations
IndiaJapanRussia

In The Last Decade

Tanuja Sheorey

30 papers receiving 577 citations

Hit Papers

VGG-ICNN: A Lightweight CNN model for crop disease identi...20222026202320242022202220234080120

Peers

Tanuja Sheorey
Comparison fields: 5 of 77
  • Plant Science 332
  • Mechanical Engineering 139
  • Analytical Chemistry 130
  • Biomedical Engineering 85
  • Computational Mechanics 53
Replace Guillermo P. Moreda Cantero with:
Guillermo P. Moreda Cantero Spain
Shuo Wei China
Shuqi Shang China
Sulaymon Eshkabilov United States
Shumao Wang China
Yuefeng Du China
Zhenwei Liang China
B. Missotten Belgium
Hongping Zhou China
Tanuja Sheorey relative to Guillermo P. Moreda Cantero Spain Guillermo P. Moreda Cantero's profile →
Citations per field
00.5×
Guillermo P. Moreda Cantero · 1×
Citations per year

Countries citing papers authored by Tanuja Sheorey

Since Specialization
Citations

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

Fields of papers citing papers by Tanuja Sheorey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tanuja Sheorey

This figure shows the co-authorship network connecting the top 25 collaborators of Tanuja Sheorey. A scholar is included among the top collaborators of Tanuja Sheorey 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 Tanuja Sheorey. Tanuja Sheorey 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
#WorkIndexed citations
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2 11
3 2
4 0
5
Vision transformer meets convolutional neural network for plant disease classificationbreakdown →
83
6 24
7 0
8 10
9 11
10 7
11 2
12 3
13
Trends in vision-based machine learning techniques for plant disease identification: A systematic reviewbreakdown →
125
14 6
15 7
16 1
17 21
18 4
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20 18

About Tanuja Sheorey

Tanuja Sheorey is a scholar working on Industrial and Manufacturing Engineering, Mechanical Engineering and Computational Mechanics, having authored 34 papers that have together received 596 indexed citations. Recurring topics across this work include Smart Agriculture and AI (6 papers), Heat Transfer Mechanisms (5 papers) and Manufacturing Process and Optimization (4 papers). The work is most often cited by research in Analytical Chemistry (130 citations), Plant Science (332 citations) and Mechanical Engineering (139 citations). Tanuja Sheorey has collaborated with scholars based in India, Japan and Russia. Frequent co-authors include Aparajita Ojha, Poornima Singh Thakur, Pritee Khanna, K. Muralidhar, Tushar Choudhary, Vijay Gupta, Harpreet Singh, Goutam Dutta, Partha P. Mukherjee and Prashant Baredar. Their work appears in journals such as Expert Systems with Applications, Applied Sciences and Journal of Energy Storage.

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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