P. Raja

2.4k total citations · 2 hit papers
39 papers, 1.7k citations indexed

About

P. Raja is a scholar working on Plant Science, Analytical Chemistry and Computer Vision and Pattern Recognition. According to data from OpenAlex, P. Raja has authored 39 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Plant Science, 11 papers in Analytical Chemistry and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in P. Raja's work include Smart Agriculture and AI (20 papers), Spectroscopy and Chemometric Analyses (11 papers) and Leaf Properties and Growth Measurement (9 papers). P. Raja is often cited by papers focused on Smart Agriculture and AI (20 papers), Spectroscopy and Chemometric Analyses (11 papers) and Leaf Properties and Growth Measurement (9 papers). P. Raja collaborates with scholars based in India, Vietnam and Spain. P. Raja's co-authors include Aravind Krishnaswamy Rangarajan, Manuel Pérez Ruiz, K. R. Aravind, Orly Enrique Apolo-Apolo, Jorge Martínez-Guanter, Gregorio Egea, Rahmath Ulla Baig, Vinh Truong Hoang, Syed Javed and K. S. Gajbhiye and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Frontiers in Plant Science.

In The Last Decade

P. Raja

36 papers receiving 1.6k citations

Hit Papers

Tomato crop disease classification using pre-trai... 2012 2026 2016 2021 2018 2012 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
P. Raja India 15 1.0k 420 415 204 187 39 1.7k
Liang Gong China 20 1.0k 1.0× 200 0.5× 296 0.7× 173 0.8× 144 0.8× 96 2.0k
Filipe Neves dos Santos Portugal 20 815 0.8× 358 0.9× 172 0.4× 188 0.9× 98 0.5× 102 1.4k
Inkyu Sa Australia 19 1.4k 1.3× 647 1.5× 386 0.9× 448 2.2× 142 0.8× 41 2.3k
Lufeng Luo China 18 1.1k 1.1× 269 0.6× 261 0.6× 143 0.7× 92 0.5× 63 1.6k
Hanwen Kang Australia 20 1.1k 1.0× 207 0.5× 260 0.6× 138 0.7× 163 0.9× 40 1.7k
Wei Ji China 22 986 1.0× 238 0.6× 300 0.7× 121 0.6× 409 2.2× 69 1.9k
Guodong Yang China 16 776 0.8× 482 1.1× 239 0.6× 204 1.0× 45 0.2× 92 1.8k
Jeremy S. Smith United Kingdom 22 519 0.5× 535 1.3× 257 0.6× 98 0.5× 203 1.1× 135 1.9k
Grzegorz Cielniak United Kingdom 22 511 0.5× 677 1.6× 131 0.3× 128 0.6× 141 0.8× 90 1.6k
Jinhui Li China 12 754 0.7× 179 0.4× 167 0.4× 98 0.5× 64 0.3× 29 1.1k

Countries citing papers authored by P. Raja

Since Specialization
Citations

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

Fields of papers citing papers by P. Raja

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of P. Raja

This figure shows the co-authorship network connecting the top 25 collaborators of P. Raja. A scholar is included among the top collaborators of P. Raja 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 P. Raja. P. Raja 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.
Raja, P., et al.. (2025). MangoYieldNet: Fruit yield estimation for mango orchards using DeepLabv3 + with ResNet18 architecture. Multimedia Tools and Applications. 84(33). 41329–41351. 1 indexed citations
2.
Raja, P., et al.. (2024). Performance analysis of modified DeepLabv3+ architecture for fruit detection and localization in apple orchards. SHILAP Revista de lepidopterología. 10. 100729–100729. 3 indexed citations
3.
Raja, P., et al.. (2023). Intelligent surface defect detection for submersible pump impeller using MobileNet V2 architecture. The International Journal of Advanced Manufacturing Technology. 124(10). 3519–3532. 5 indexed citations
4.
Raja, A., et al.. (2023). Morphological Evaluation of the Local Genotypes of Broad Bean (Vicia faba L.) for Yield Attributes under the Nilgiris Condition. International Journal of Environment and Climate Change. 13(10). 1916–1924. 1 indexed citations
5.
Raja, P., et al.. (2022). Intelligent yield estimation for tomato crop using SegNet with VGG19 architecture. Scientific Reports. 12(1). 13601–13601. 11 indexed citations
6.
Raja, P., et al.. (2021). Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques—A Review. Frontiers in Plant Science. 12. 684328–684328. 91 indexed citations
7.
Rangarajan, Aravind Krishnaswamy, et al.. (2021). Crop identification and disease classification using traditional machine learning and deep learning approaches. Journal of Engineering Research. 11(1). 228–252. 9 indexed citations
8.
Rangarajan, Aravind Krishnaswamy, P. Raja, & Manuel Pérez Ruiz. (2021). Disease classification in aubergine with local symptomatic region using deep learning models. Biosystems Engineering. 209. 139–153. 18 indexed citations
9.
Rangarajan, Aravind Krishnaswamy & P. Raja. (2020). Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM. Scientific Reports. 10(1). 2322–2322. 145 indexed citations
10.
Apolo-Apolo, Orly Enrique, Jorge Martínez-Guanter, Gregorio Egea, P. Raja, & Manuel Pérez Ruiz. (2020). Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. European Journal of Agronomy. 115. 126030–126030. 190 indexed citations
11.
Aravind, K. R., et al.. (2019). Disease classification in Solanum melongena using deep learning. Spanish Journal of Agricultural Research. 17(3). e0204–e0204. 15 indexed citations
12.
Raja, P., et al.. (2018). Real time navigation of a mobile robot. International Journal of Advanced Intelligence Paradigms. 11(3/4). 348–348. 1 indexed citations
13.
Raja, P., et al.. (2017). Optimization of rollover stability for a three-wheeler vehicle. Advances in Manufacturing. 5(3). 279–288. 7 indexed citations
14.
Raja, P., et al.. (2014). Quadrant based incremental planning for mobile robots. Journal of Central South University. 21(5). 1792–1803. 2 indexed citations
15.
Raja, P., et al.. (2012). ON-LINE PATH PLANNING FOR MOBILE ROBOTS IN DYNAMIC ENVIRONMENTS. Neural Network World. 22(1). 67–83. 13 indexed citations
16.
Raja, P., et al.. (2011). Path planning for a mobile robot in dynamic environments. International Journal of the Physical Sciences. 6(20). 4721–4731. 31 indexed citations
17.
Raja, P., et al.. (2009). Path Planning for Mobile Robots in Dynamic Environments Using Particle Swarm Optimization. 401–405. 44 indexed citations
18.
Raja, P., et al.. (2008). Weather based forecasting model for incidence of downy mildew of pearl millet in Coimbatore. Indian Phytopathology. 61(1). 60–64. 1 indexed citations
19.
Raja, P., et al.. (2008). Path Planning for a Mobile Robot to Avoid Polyhedral and Curved Obstacles. 9(2). 31–41. 7 indexed citations
20.
Raja, P., et al.. (2004). Soil–site suitability evaluation in two different Agroecological systems and relevance of the parameters. Journal of the Indian Society of Soil Science. 52(2). 177–183.

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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