Sajad Sabzi

1.5k total citations
61 papers, 1.1k citations indexed

About

Sajad Sabzi is a scholar working on Analytical Chemistry, Plant Science and Biomedical Engineering. According to data from OpenAlex, Sajad Sabzi has authored 61 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Analytical Chemistry, 49 papers in Plant Science and 11 papers in Biomedical Engineering. Recurrent topics in Sajad Sabzi's work include Spectroscopy and Chemometric Analyses (52 papers), Smart Agriculture and AI (33 papers) and Leaf Properties and Growth Measurement (17 papers). Sajad Sabzi is often cited by papers focused on Spectroscopy and Chemometric Analyses (52 papers), Smart Agriculture and AI (33 papers) and Leaf Properties and Growth Measurement (17 papers). Sajad Sabzi collaborates with scholars based in Iran, Spain and Mexico. Sajad Sabzi's co-authors include Yousef Abbaspour‐Gilandeh, Juan Ignacio Arribas, Ginés Garcı́a-Mateos, Razieh Pourdarbani, José Miguel Molina‐Martínez, Hossein Javadikia, Davood Kalantari, José Luis Hernández-Hernández, Mohammad Hossein Rohban and Jitendra Paliwal and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing and Journal of Food Science.

In The Last Decade

Sajad Sabzi

59 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sajad Sabzi Iran 22 745 625 174 152 120 61 1.1k
Yuanyuan Shao China 20 489 0.7× 540 0.9× 202 1.2× 113 0.7× 135 1.1× 56 1.1k
Jingzhu Wu China 16 740 1.0× 689 1.1× 299 1.7× 162 1.1× 143 1.2× 40 1.6k
P.P. Subedi Australia 17 637 0.9× 716 1.1× 186 1.1× 124 0.8× 136 1.1× 43 1.2k
Bahareh Jamshidi Iran 16 531 0.7× 700 1.1× 237 1.4× 108 0.7× 174 1.4× 31 1.1k
Belén Diezma Iglesias Spain 16 533 0.7× 717 1.1× 304 1.7× 212 1.4× 192 1.6× 81 1.2k
Hanping Mao China 18 462 0.6× 604 1.0× 247 1.4× 122 0.8× 107 0.9× 63 1.1k
Insuck Baek United States 21 535 0.7× 808 1.3× 296 1.7× 203 1.3× 205 1.7× 104 1.4k
Aichen Wang China 16 803 1.1× 310 0.5× 117 0.7× 235 1.5× 106 0.9× 43 1.2k
W. Mazurek Poland 13 385 0.5× 620 1.0× 220 1.3× 81 0.5× 157 1.3× 41 949

Countries citing papers authored by Sajad Sabzi

Since Specialization
Citations

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

Fields of papers citing papers by Sajad Sabzi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sajad Sabzi

This figure shows the co-authorship network connecting the top 25 collaborators of Sajad Sabzi. A scholar is included among the top collaborators of Sajad Sabzi 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 Sajad Sabzi. Sajad Sabzi 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.
Pourdarbani, Razieh, et al.. (2025). Modeling Global Warming from Agricultural CO2 Emissions: From Worldwide Patterns to the Case of Iran. Modelling—International Open Access Journal of Modelling in Engineering Science. 6(4). 153–153.
2.
Sabzi, Sajad, et al.. (2024). Classification of Healthy and Frozen Pomegranates Using Hyperspectral Imaging and Deep Learning. Horticulturae. 10(1). 43–43. 9 indexed citations
3.
Sabzi, Sajad, et al.. (2023). Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection. Journal of Food Science. 88(12). 5149–5163. 16 indexed citations
4.
Pourdarbani, Razieh, et al.. (2023). From Harvest to Market: Non-Destructive Bruise Detection in Kiwifruit Using Convolutional Neural Networks and Hyperspectral Imaging. Horticulturae. 9(8). 936–936. 13 indexed citations
5.
Pourdarbani, Razieh, et al.. (2023). Examination of Lemon Bruising Using Different CNN-Based Classifiers and Local Spectral-Spatial Hyperspectral Imaging. Algorithms. 16(2). 113–113. 14 indexed citations
6.
Pourdarbani, Razieh, et al.. (2021). One-Dimensional Convolutional Neural Networks for Hyperspectral Analysis of Nitrogen in Plant Leaves. Applied Sciences. 11(24). 11853–11853. 12 indexed citations
7.
Sabzi, Sajad, Razieh Pourdarbani, Mohammad Hossein Rohban, et al.. (2021). Early Detection of Excess Nitrogen Consumption in Cucumber Plants Using Hyperspectral Imaging Based on Hybrid Neural Networks and the Imperialist Competitive Algorithm. Agronomy. 11(3). 575–575. 19 indexed citations
8.
9.
Sabzi, Sajad, Razieh Pourdarbani, Mohammad Hossein Rohban, Ginés Garcı́a-Mateos, & Juan Ignacio Arribas. (2021). Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions. Chemometrics and Intelligent Laboratory Systems. 217. 104404–104404. 35 indexed citations
10.
Sabzi, Sajad, et al.. (2021). Detection of snail pest in citrus orchard under different lighting conditions using deep neural networks. Food Science and Technology. 18(115). 157–169. 1 indexed citations
11.
Pourdarbani, Razieh, Sajad Sabzi, & Juan Ignacio Arribas. (2021). Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data. Heliyon. 7(9). e07942–e07942. 23 indexed citations
12.
13.
Abbaspour‐Gilandeh, Yousef, et al.. (2020). A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars. Agronomy. 10(1). 117–117. 33 indexed citations
14.
Sabzi, Sajad, Yousef Abbaspour‐Gilandeh, & Juan Ignacio Arribas. (2020). An automatic visible-range video weed detection, segmentation and classification prototype in potato field. Heliyon. 6(5). e03685–e03685. 47 indexed citations
15.
Abbaspour‐Gilandeh, Yousef, et al.. (2020). Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields. Plants. 9(5). 559–559. 56 indexed citations
16.
Sabzi, Sajad, Yousef Abbaspour‐Gilandeh, & Hossein Javadikia. (2018). Detection of Two Types of Weed through Machine Vision System: Improving Site-Specific Spraying. SHILAP Revista de lepidopterología.
17.
Sabzi, Sajad, et al.. (2017). Study of the yield and some important plant of common bean (Phaseolus vulgaris) genotypes at different moisture levels.. 10(1). 1 indexed citations
18.
Javadikia, Hossein, et al.. (2017). Machine vision based expert system to estimate orange mass of three varieties. International journal of agricultural and biological engineering. 10(2). 132–139. 5 indexed citations
19.
Sabzi, Sajad, Yousef Abbaspour‐Gilandeh, & Juan Ignacio Arribas. (2017). Non-intrusive image processing Thompson orange grading methods. 35–39. 5 indexed citations
20.
Sabzi, Sajad, et al.. (2013). Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision. Measurement. 46(9). 3333–3341. 30 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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