Yash Dixit

2.2k citations
41 papers · 1.5k · 1 hit paper · h-index 19

Impact in

Papers in

Yash Dixit

41 papers receiving 1.5k citations

Yash Dixit's Hit Papers

IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry 2020 · 535 citations
5350+2+4Years since publication100200300400500

Peers

Yash Dixit
Comparison fields: 5 of 128
  • Analytical Chemistry 701
  • Animal Science and Zoology 244
  • Biophysics 113
  • Mechanics of Materials 323
  • Food Science 215
Replace El Mostafa Qannari with:
El Mostafa Qannari France
Rommel Barbosa Brazil
Knut Kvaal Norway
Jun‐Li Xu Ireland
J.M. Prats-Montalbán Spain
Choong-Yeun Liong Malaysia
Loong Chuen Lee Malaysia
Annamalai Manickavasagan Canada
Juan Gómez‐Sanchís Spain
Rui C. Martins Portugal
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Citations per field
00.5×10×14.7×
El Mostafa Qannari · 1×
Citations per year

Countries citing papers authored by Yash Dixit

Since Specialization
Citations

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

Fields of papers citing papers by Yash Dixit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yash Dixit, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yash Dixit Line = papers co-authored together Yash Dixit links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1
IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry
Hit paper breakdown →
2020535
2 2017199
3 201795
4 202289
5 202054
6 201749
7 201744
8 201942
9 201736
10 201635
11 202334
12 202332
13 202030
14 201626
15 201723
16 201720
17 201720
18 201718
19 201518
20 202317

About Yash Dixit

Yash Dixit is a scholar working on Analytical Chemistry, Animal Science and Zoology, Biophysics, Biomedical Engineering and Mechanics of Materials, having authored 41 papers that have together received 1.5k indexed citations. Recurring topics across this work include Spectroscopy and Chemometric Analyses (27 papers), Meat and Animal Product Quality (14 papers), Spectroscopy Techniques in Biomedical and Chemical Research (12 papers), Advanced Chemical Sensor Technologies (12 papers), Laser-induced spectroscopy and plasma (9 papers), Analytical chemistry methods development (7 papers), Identification and Quantification in Food (6 papers) and Cultural Heritage Materials Analysis (4 papers). The work is most often cited by research in Analytical Chemistry (701 citations), Animal Science and Zoology (244 citations), Biophysics (113 citations), Mechanics of Materials (323 citations) and Food Science (215 citations). Yash Dixit has collaborated with scholars based in New Zealand, Australia and Ireland. Frequent co-authors include Patrick J. Cullen, Carl Sullivan, N.N. Misra, Alex Martynenko, Rohit Upadhyay, Manreet Bhullar, Ahmad Al-Mallahi, Raquel Cama‐Moncunill, Maria P. Casado‐Gavalda and Xavier Cama-Moncunill. Their work appears in journals such as Meat Science, Food Research International, Journal of Food Engineering, Comprehensive Reviews in Food Science and Food Safety and Food Chemistry.

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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