Manika Manwal
Impact in
-
- Spectroscopy and Chemometric Analyses
Papers in
-
- Smart Agriculture and AI 25
- Plant Disease Management Techniques 12
- Plant Virus Research Studies 7
- Date Palm Research Studies 4
-
- AI in cancer detection 4
- Co-authors
- Vinay Kukreja (41 shared papers)Rishabh Sharma (24 shared papers)Shiva Mehta (15 shared papers)Amit Gupta (1 shared paper)Deepak Upadhyay (10 shared papers)Satvik Vats (3 shared papers)Arpit Jain (2 shared papers)Vikrant Sharma (6 shared papers)
- Journals
- Materials Today Proceedings (1 paper)Türk bilgisayar ve matematik eğitimi dergisi (1 paper)Measurement Sensors (1 paper)2022 IEEE Delhi Section Conference (DELCON) (1 paper)
- Partner nations
- IndiaSaudi ArabiaCameroon
In The Last Decade
Manika Manwal
58 papers receiving 192 citations
Peers
Comparison fields: 5 of 71
- Analytical Chemistry 15
- Health Information Management 6
- Artificial Intelligence 41
- Plant Science 43
- Computer Vision and Pattern Recognition 22
Countries citing papers authored by Manika Manwal
This map shows the geographic impact of Manika Manwal'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 Manika Manwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manika Manwal more than expected).
Fields of papers citing papers by Manika Manwal
This network shows the impact of papers produced by Manika Manwal. 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 Manika Manwal. The network helps show where Manika Manwal may publish in the future.
Co-authors
The 25 scholars most cited alongside Manika Manwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 77 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 15 | |
| 2 | 2023 | 12 | |
| 3 | 2024 | 12 | |
| 4 | 2023 | 11 | |
| 5 | 2024 | 10 | |
| 6 | 2017 | 9 | |
| 7 | 2023 | 8 | |
| 8 | 2023 | 8 | |
| 9 | 2024 | 7 | |
| 10 | 2024 | 6 | |
| 11 | 2021 | 6 | |
| 12 | 2024 | 6 | |
| 13 | 2022 | 5 | |
| 14 | 2023 | 5 | |
| 15 | 2024 | 4 | |
| 16 | 2024 | 4 | |
| 17 | 2023 | 3 | |
| 18 | 2024 | 3 | |
| 19 | 2024 | 3 | |
| 20 | 2024 | 3 |
About Manika Manwal
Manika Manwal is a scholar working on Plant Science, Artificial Intelligence, Computer Networks and Communications, Analytical Chemistry and Computer Vision and Pattern Recognition, having authored 77 papers that have together received 199 indexed citations. Recurring topics across this work include Smart Agriculture and AI (25 papers), Plant Disease Management Techniques (12 papers), Spectroscopy and Chemometric Analyses (8 papers), Plant Virus Research Studies (7 papers), Artificial Intelligence in Healthcare (6 papers), Plant Pathogens and Fungal Diseases (4 papers), Date Palm Research Studies (4 papers) and AI in cancer detection (4 papers). The work is most often cited by research in Analytical Chemistry (15 citations), Health Information Management (6 citations), Artificial Intelligence (41 citations), Plant Science (43 citations) and Computer Vision and Pattern Recognition (22 citations). Manika Manwal has collaborated with scholars based in India, Saudi Arabia and Cameroon. Frequent co-authors include Vinay Kukreja, Rishabh Sharma, Shiva Mehta, Amit Gupta, Deepak Upadhyay, Satvik Vats, Arpit Jain, Vikrant Sharma, Kireet Joshi and Shweta Kumari. Their work appears in journals such as Materials Today Proceedings, Türk bilgisayar ve matematik eğitimi dergisi, Measurement Sensors and 2022 IEEE Delhi Section Conference (DELCON).
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.