M. M. Pathan

1.0k citations
44 papers · 494 · 2 hit papers · h-index 8

Impact in

Papers in

M. M. Pathan

38 papers receiving 469 citations

M. M. Pathan's Hit Papers

Agricultural plant diseases identification: From traditional approach to deep learning 2023 · 117 citations
1170+1+2Years since publication255075100

Peers

M. M. Pathan
Comparison fields: 5 of 104
  • Animal Science and Zoology 103
  • Agronomy and Crop Science 83
  • Analytical Chemistry 45
  • Small Animals 33
  • Plant Science 111
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Countries citing papers authored by M. M. Pathan

Since Specialization
Citations

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

Fields of papers citing papers by M. M. Pathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside M. M. Pathan, 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 M. M. Pathan Line = papers co-authored together M. M. Pathan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Agricultural plant diseases identification: From traditional approach to deep learning
Hit paper breakdown →
2023117
2 201786
3
Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification
Hit paper breakdown →
202381
4 201242
5 202232
6 202223
7 202014
8 20247
9 20117
10 20187
11
Heat Shock Proteins and their clinical Implications
20106
12 20226
13
COMPARATIVE STUDIES ON HAEMATO-BIOCHEMICAL PROFILE OF CYCLIC AND NON-CYCLIC HOLSTEIN-FRIESIAN CROSS-BRED COWS
20115
14
Bovine colostrum and neonate immunity - a review.
20115
15
ANTIOXIDANT STATUS IN PERIPARTURIENT MEHSANA BUFFALOES
20105
16 20245
17 20245
18 20254
19 20244
20 20214

About M. M. Pathan

M. M. Pathan is a scholar working on Agronomy and Crop Science, Animal Science and Zoology, Plant Science, Genetics and Artificial Intelligence, having authored 44 papers that have together received 494 indexed citations. Recurring topics across this work include Effects of Environmental Stressors on Livestock (11 papers), Reproductive Physiology in Livestock (10 papers), Smart Agriculture and AI (9 papers), Genetic and phenotypic traits in livestock (7 papers), Livestock Management and Performance Improvement (5 papers), Spectroscopy and Chemometric Analyses (4 papers), Cinema and Media Studies (2 papers) and Animal Nutrition and Health (2 papers). The work is most often cited by research in Animal Science and Zoology (103 citations), Agronomy and Crop Science (83 citations), Analytical Chemistry (45 citations), Small Animals (33 citations) and Plant Science (111 citations). M. M. Pathan has collaborated with scholars based in India, South Korea and Saudi Arabia. Frequent co-authors include Ramgopal Kashyap, Jameer Kotwal, Y. G. Patel, Raghvendra Kumar, Muhammad Fazal Ijaz, Jana Shafi, Yogesh Kumar, SeongKi Kim, Parikshit N. Mahalle and Nilanjan Dey. Their work appears in journals such as Potato Research, Multimedia Tools and Applications, Data in Brief, IEEE Access and Journal of Animal Physiology and Animal Nutrition.

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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