Sapna Nigam

449 total citations
13 papers, 266 citations indexed

About

Sapna Nigam is a scholar working on Plant Science, Analytical Chemistry and Genetics. According to data from OpenAlex, Sapna Nigam has authored 13 papers receiving a total of 266 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Plant Science, 6 papers in Analytical Chemistry and 2 papers in Genetics. Recurrent topics in Sapna Nigam's work include Smart Agriculture and AI (10 papers), Spectroscopy and Chemometric Analyses (6 papers) and Plant Disease Management Techniques (4 papers). Sapna Nigam is often cited by papers focused on Smart Agriculture and AI (10 papers), Spectroscopy and Chemometric Analyses (6 papers) and Plant Disease Management Techniques (4 papers). Sapna Nigam collaborates with scholars based in India and Bulgaria. Sapna Nigam's co-authors include Rajni Jain, Alka Arora, Sudeep Marwaha, Md. Ashraful Haque, Vaibhav Kumar Singh, K. S. Hooda, Sumit Kumar Aggarwal, Brejesh Lall, Mukesh Kumar and Prabhat Kumar and has published in prestigious journals such as Scientific Reports, Frontiers in Plant Science and Neural Computing and Applications.

In The Last Decade

Sapna Nigam

9 papers receiving 253 citations

Peers

Sapna Nigam

PS

AC

ECOLO

CB

FS

Malusi Sibiya South Africa

José G. M. Esgario Brazil

N Shashank India

Vibhor Kumar Vishnoi India

Sergio Rodríguez-Vaamonde Spain

R Pramodhini India

Jitong Cai China

Sivalingam Elayabalan India

Hiroyuki Uga Japan

Ramachandra Hebbar India

Malusi Sibiya South Africa

Sapna Nigam

218 ×0.9

PS

106 ×1.1

AC

33 ×0.9

ECOLO

9 ×0.6

CB

7 ×0.5

FS

Citations per year, relative to Sapna Nigam Sapna Nigam (= 1×) peers Malusi Sibiya

Countries citing papers authored by Sapna Nigam

Since Specialization

Citations

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

Fields of papers citing papers by Sapna Nigam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sapna Nigam

This figure shows the co-authorship network connecting the top 25 collaborators of Sapna Nigam. A scholar is included among the top collaborators of Sapna Nigam 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 Sapna Nigam. Sapna Nigam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Nigam, Sapna, et al.. (2025). Automated weed classification using attention-embedded ConvNeXtV2 architecture. Procedia Computer Science. 260. 291–299.

2.

Bashyal, Bishnu Maya, et al.. (2025). Molecular validation integrated reliable deep learning approach for early blight severity detection in tomato crops under protected cultivation environments. Results in Engineering. 28. 107739–107739.

3.

Nigam, Sapna, Rajni Jain, Vaibhav Kumar Singh, Ashish Kumar Singh, & Hari Krishna. (2025). Automated severity level estimation of wheat rust using an EfficientNet-CBAM hybrid model. Frontiers in Plant Science. 16. 1540642–1540642.

4.

Nigam, Sapna, Rajni Jain, Vaibhav Kumar Singh, et al.. (2024). EfficientNet architecture and attention mechanism-based wheat disease identification model. Procedia Computer Science. 235. 383–393. 14 indexed citations

5.

Ray, Mrinmoy, et al.. (2024). Deep learning models for detection and classification of spongy tissue disorder in mango using X-ray images. Journal of Food Measurement & Characterization. 18(9). 7806–7818. 3 indexed citations

6.

Nigam, Sapna, Rajni Jain, Sudeep Marwaha, et al.. (2023). Deep transfer learning model for disease identification in wheat crop. Ecological Informatics. 75. 102068–102068. 70 indexed citations

7.

Haque, Md. Ashraful, Sudeep Marwaha, Sapna Nigam, et al.. (2022). Deep learning-based approach for identification of diseases of maize crop. Scientific Reports. 12(1). 6334–6334. 90 indexed citations

8.

Haque, Md. Ashraful, et al.. (2022). A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize. Frontiers in Plant Science. 13. 1077568–1077568. 18 indexed citations

9.

Godara, Samarth, et al.. (2022). Applications of Data Mining in the Bioinformatics Field: A Review. Bhartiya Krishi Anusandhan Patrika. 1 indexed citations

10.

Haque, Md. Ashraful, et al.. (2022). Recognition of diseases of maize crop using deep learning models. Neural Computing and Applications. 35(10). 7407–7421. 25 indexed citations

11.

Nigam, Sapna, Rajni Jain, Sudeep Marwaha, et al.. (2021). Automating yellow rust disease identification in wheat using artificial intelligence. The Indian Journal of Agricultural Sciences. 91(9). 1391–1395. 11 indexed citations

12.

Nigam, Sapna & Rajni Jain. (2020). Plant disease identification using Deep Learning: A review. The Indian Journal of Agricultural Sciences. 90(2). 249–257. 33 indexed citations

13.

McDonald, David, et al.. (1988). Coordinated research on groundnut rosette virus disease: summary proceedings of the Consultative Group Meeting, Lilongwe, Malawi, 8-10 March 1987. Open Access Repository of ICRISAT (International Crops Research Institute for the Semi-Arid Tropics). 1 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.

Explore authors with similar magnitude of impact