Naseeb Singh

582 total citations
40 papers, 336 citations indexed

About

Naseeb Singh is a scholar working on Plant Science, Analytical Chemistry and Mechanical Engineering. According to data from OpenAlex, Naseeb Singh has authored 40 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Plant Science, 9 papers in Analytical Chemistry and 6 papers in Mechanical Engineering. Recurrent topics in Naseeb Singh's work include Smart Agriculture and AI (16 papers), Spectroscopy and Chemometric Analyses (9 papers) and Natural Products and Biological Research (5 papers). Naseeb Singh is often cited by papers focused on Smart Agriculture and AI (16 papers), Spectroscopy and Chemometric Analyses (9 papers) and Natural Products and Biological Research (5 papers). Naseeb Singh collaborates with scholars based in India, Switzerland and United States. Naseeb Singh's co-authors include V.K. Tewari, C.M. Pareek, Dev Kumar Das, A. K. Sinha, Prabir Kumar Biswas, Rakesh Bhardwaj, Simardeep Kaur, Burhan U. Choudhury, Amritbir Riar and Amit Kumar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Food Research International and Planta.

In The Last Decade

Naseeb Singh

33 papers receiving 321 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Naseeb Singh India 11 160 79 57 52 34 40 336
Madaín Pérez‐Patricio Mexico 9 77 0.5× 19 0.2× 61 1.1× 46 0.9× 29 0.9× 30 305
Junxiong Zhang China 14 391 2.4× 129 1.6× 25 0.4× 28 0.5× 90 2.6× 54 619
Yuanyuan Zhao China 10 122 0.8× 24 0.3× 34 0.6× 92 1.8× 20 0.6× 35 411
Jianwei Ji China 11 184 1.1× 20 0.3× 17 0.3× 83 1.6× 40 1.2× 54 375
Jiangming Jia China 10 173 1.1× 81 1.0× 39 0.7× 11 0.2× 61 1.8× 24 348
Shuhuai Zhang Japan 9 107 0.7× 157 2.0× 93 1.6× 185 3.6× 10 0.3× 37 440
Suming Chen Taiwan 16 190 1.2× 174 2.2× 23 0.4× 74 1.4× 35 1.0× 44 598
John Reidar Mathiassen Norway 14 60 0.4× 122 1.5× 54 0.9× 11 0.2× 26 0.8× 24 507
Xiaoli Jia China 19 187 1.2× 38 0.5× 110 1.9× 174 3.3× 82 2.4× 73 1.0k

Countries citing papers authored by Naseeb Singh

Since Specialization
Citations

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

Fields of papers citing papers by Naseeb Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Naseeb Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Naseeb Singh. A scholar is included among the top collaborators of Naseeb Singh 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 Naseeb Singh. Naseeb Singh 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
1.
2.
Kaur, Simardeep, Naseeb Singh, Paras Sharma, et al.. (2024). Optimizing protein content prediction in rice bean (Vigna umbellata L.) by integrating near-infrared reflectance spectroscopy, MPLS, deep learning, and key wavelengths selection algorithms. Journal of Food Composition and Analysis. 135. 106655–106655. 10 indexed citations
3.
Singh, Naseeb, Simardeep Kaur, Amit Kumar, et al.. (2024). Integrating NIR spectroscopy with machine learning and heuristic algorithm-assisted wavelength selection algorithms for protein content prediction in rice bean (Vigna umbellata L.). Food and Humanity. 3. 100399–100399. 7 indexed citations
4.
Kaur, Simardeep, Samarth Godara, Naseeb Singh, et al.. (2024). Multivariate Data Analysis Assisted Mining of Nutri-rich Genotypes from North Eastern Himalayan Germplasm Collection of Perilla (Perilla frutescens L.). Plant Foods for Human Nutrition. 79(4). 843–850. 5 indexed citations
5.
Singhal, Gaurav, Burhan U. Choudhury, Naseeb Singh, & Jonali Goswami. (2024). An enhanced chlorophyll estimation model with a canopy structural trait in maize crops: Use of multi-spectral UAV images and machine learning algorithm. Ecological Informatics. 83. 102811–102811. 9 indexed citations
6.
7.
Singh, Naseeb, V.K. Tewari, & Prabir Kumar Biswas. (2024). Vision transformers for cotton boll segmentation: Hyperparameters optimization and comparison with convolutional neural networks. Industrial Crops and Products. 223. 120241–120241. 1 indexed citations
8.
Kaur, Simardeep, Arti Kumari, Karishma Seem, et al.. (2024). Finger millet (Eleusine coracana L.): from staple to superfood—a comprehensive review on nutritional, bioactive, industrial, and climate resilience potential. Planta. 260(3). 75–75. 4 indexed citations
9.
Singh, Naseeb, et al.. (2024). Development of Attention-Enabled Multi-Scale Pyramid Network-Based Models for Body Part Segmentation of Dairy Cows. Journal of Biosystems Engineering. 49(2). 186–201. 3 indexed citations
10.
Kaur, Simardeep, Naseeb Singh, Maharishi Tomar, et al.. (2024). NIRS-based prediction modeling for nutritional traits in Perilla germplasm from NEH Region of India: comparative chemometric analysis using mPLS and deep learning. Journal of Food Measurement & Characterization. 18(11). 9019–9035. 8 indexed citations
11.
Kaur, Simardeep, Naseeb Singh, Amit Kumar, et al.. (2024). Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm. Applied Food Research. 4(2). 100607–100607. 8 indexed citations
12.
Singh, Naseeb, et al.. (2024). ProTformer: Transformer-based model for superior prediction of protein content in lablab bean (Lablab purpureus L.) using Near-Infrared Reflectance spectroscopy. Food Research International. 197(Pt 1). 115161–115161. 11 indexed citations
13.
14.
Singh, Naseeb, et al.. (2024). In-field performance evaluation of robotic arm developed for harvesting cotton bolls. Computers and Electronics in Agriculture. 227. 109517–109517. 5 indexed citations
15.
Choudhury, Burhan U., et al.. (2023). Spectral library of crops and discrimination of major vegetables grown in the eastern Himalayan ecosystem: A proximal hyperspectral remote sensing approach. Ecological Informatics. 77. 102263–102263. 13 indexed citations
16.
Choudhury, Burhan U., et al.. (2023). Development of land-use-specific pedotransfer functions for predicting bulk density of acidic topsoil in eastern Himalayas (India). Geoderma Regional. 34. e00671–e00671. 4 indexed citations
17.
Singh, Naseeb, et al.. (2023). Optimizing cotton‐picking robotic manipulator and inverse kinematics modeling using evolutionary algorithm‐assisted artificial neural network. Journal of Field Robotics. 41(7). 2322–2342. 4 indexed citations
18.
Mani, Indra, et al.. (2023). Parameter optimization for selective harvesting in cauliflower (Brassica oleracea) using response surface methodology. SHILAP Revista de lepidopterología. 93(8). 912–918. 5 indexed citations
19.
Chakraborty, Debasish, Saurav Saha, Naseeb Singh, et al.. (2022). Usability of the Weather Forecast for Tackling Climatic Variability and Its Effect on Maize Crop Yield in Northeastern Hill Region of India. Agronomy. 12(10). 2529–2529. 9 indexed citations
20.
Das, Dev Kumar, Naseeb Singh, & A. K. Sinha. (2006). A comparison of Fourier transform and wavelet transform methods for detection and classification of faults on transmission lines. 7 pp.–7 pp.. 54 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.

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