Ahmad Al-Mallahi

1.6k total citations · 1 hit paper
26 papers, 1.1k citations indexed

About

Ahmad Al-Mallahi is a scholar working on Plant Science, Analytical Chemistry and Biomedical Engineering. According to data from OpenAlex, Ahmad Al-Mallahi has authored 26 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Plant Science, 11 papers in Analytical Chemistry and 6 papers in Biomedical Engineering. Recurrent topics in Ahmad Al-Mallahi's work include Smart Agriculture and AI (16 papers), Spectroscopy and Chemometric Analyses (11 papers) and Advanced Chemical Sensor Technologies (4 papers). Ahmad Al-Mallahi is often cited by papers focused on Smart Agriculture and AI (16 papers), Spectroscopy and Chemometric Analyses (11 papers) and Advanced Chemical Sensor Technologies (4 papers). Ahmad Al-Mallahi collaborates with scholars based in Canada, Japan and United Kingdom. Ahmad Al-Mallahi's co-authors include Manreet Bhullar, Alex Martynenko, N.N. Misra, Rohit Upadhyay, Yash Dixit, Takao Kataoka, Rui Li, Longsheng Fu, Yongjie Cui and Jaemyung Shin and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Internet of Things Journal and Computers and Electronics in Agriculture.

In The Last Decade

Ahmad Al-Mallahi

26 papers receiving 1.1k citations

Hit Papers

IoT, Big Data, and Artificial Intelligence in Agriculture... 2020 2026 2022 2024 2020 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ahmad Al-Mallahi Canada 12 630 224 169 103 102 26 1.1k
Baoxing Gu China 12 440 0.7× 173 0.8× 64 0.4× 101 1.0× 57 0.6× 22 734
Guangzhao Tian China 11 403 0.6× 152 0.7× 65 0.4× 76 0.7× 53 0.5× 22 669
Κωνσταντίνος Λιάκος Greece 6 1.1k 1.7× 287 1.3× 167 1.0× 66 0.6× 377 3.7× 13 1.8k
Jingbin Li China 16 257 0.4× 84 0.4× 103 0.6× 77 0.7× 53 0.5× 74 796
Simone Figorilli Italy 16 274 0.4× 111 0.5× 337 2.0× 165 1.6× 136 1.3× 50 1.2k
Loredana Lunadei Spain 9 313 0.5× 158 0.7× 342 2.0× 199 1.9× 50 0.5× 12 1.1k
Abhinav Sharma India 17 438 0.7× 72 0.3× 60 0.4× 48 0.5× 109 1.1× 65 1.4k
Huarui Wu China 15 648 1.0× 202 0.9× 44 0.3× 55 0.5× 89 0.9× 74 1.0k
Francisco Rovira-Más Spain 18 875 1.4× 64 0.3× 98 0.6× 43 0.4× 249 2.4× 62 1.4k
Remigio Berruto Italy 14 460 0.7× 57 0.3× 87 0.5× 139 1.3× 136 1.3× 72 1.1k

Countries citing papers authored by Ahmad Al-Mallahi

Since Specialization
Citations

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

Fields of papers citing papers by Ahmad Al-Mallahi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ahmad Al-Mallahi. 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 Ahmad Al-Mallahi. The network helps show where Ahmad Al-Mallahi may publish in the future.

Co-authorship network of co-authors of Ahmad Al-Mallahi

This figure shows the co-authorship network connecting the top 25 collaborators of Ahmad Al-Mallahi. A scholar is included among the top collaborators of Ahmad Al-Mallahi 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 Ahmad Al-Mallahi. Ahmad Al-Mallahi 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.
Al-Mallahi, Ahmad, et al.. (2025). Accurate estimation of tuber size in large potato throughput at potato storage using machine vision and machine learning techniques. Smart Agricultural Technology. 10. 100860–100860. 2 indexed citations
2.
Esau, Travis J., et al.. (2025). Time-of-flight-based advanced surface reconstruction methods for real-time volume estimation of bulk harvested wild blueberries. Smart Agricultural Technology. 11. 101050–101050. 1 indexed citations
4.
Al-Mallahi, Ahmad, et al.. (2024). Development of a flexible electronic control unit for seamless integration of machine vision to CAN-enabled boom sprayers for spot application technology. SHILAP Revista de lepidopterología. 9. 100618–100618. 1 indexed citations
5.
Al-Mallahi, Ahmad, et al.. (2024). Calibrating lab and field reflectance spectra for nutrient estimation in potato plants using local support vector regression models. SHILAP Revista de lepidopterología. 8. 100492–100492. 2 indexed citations
6.
Avulapati, Madan Mohan, et al.. (2024). A Low-Cost, Reusable All-Solid-State TiN-Based EGFET Soil pH Sensor. IEEE Sensors Journal. 25(3). 4184–4191. 3 indexed citations
7.
Al-Mallahi, Ahmad, et al.. (2023). Development of robust communication algorithm between machine vision and boom sprayer for spot application via ISO 11783. SHILAP Revista de lepidopterología. 4. 100212–100212. 4 indexed citations
8.
Esau, Travis J., et al.. (2023). Use of a Time-of-Flight 3D Camera for Real-Time Nonporous Bulk Volume Estimation. 1 indexed citations
10.
Al-Mallahi, Ahmad, et al.. (2022). Development of Robust Communication Algorithm between Machine Vision and Boom Sprayer for Spot Application Via Iso11783. SSRN Electronic Journal. 1 indexed citations
11.
Al-Mallahi, Ahmad, et al.. (2022). Automatic imaging system mounted on boom sprayer for crop scouting using an off-the-shelf RGB camera. Computers and Electronics in Agriculture. 193. 106690–106690. 3 indexed citations
12.
Shin, Jaemyung, Young Chang, Brandon Heung, et al.. (2021). A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Computers and Electronics in Agriculture. 183. 106042–106042. 127 indexed citations
13.
Misra, N.N., Yash Dixit, Ahmad Al-Mallahi, et al.. (2020). IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet of Things Journal. 9(9). 6305–6324. 511 indexed citations breakdown →
14.
Shin, Jaemyung, Young Chang, Brandon Heung, et al.. (2020). Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection. Biosystems Engineering. 194. 49–60. 41 indexed citations
15.
Liu, Zhihao, Yali Feng, Rui Li, et al.. (2019). Improved kiwifruit detection using VGG16 with RGB and NIR information fusion. 2019 Boston, Massachusetts July 7- July 10, 2019. 3 indexed citations
16.
Fu, Longsheng, ElKamil Tola, Ahmad Al-Mallahi, Rui Li, & Yongjie Cui. (2019). A novel image processing algorithm to separate linearly clustered kiwifruits. Biosystems Engineering. 183. 184–195. 104 indexed citations
17.
Al-Mallahi, Ahmad & Takashi Kataoka. (2013). Monitoring the Flow of Seeds in Grain Drill using Fiber Sensor. IFAC Proceedings Volumes. 46(18). 311–314. 1 indexed citations
18.
Al-Mallahi, Ahmad, et al.. (2009). An image processing algorithm for detecting in-line potato tubers without singulation. Computers and Electronics in Agriculture. 70(1). 239–244. 25 indexed citations
19.
Al-Mallahi, Ahmad, et al.. (2009). Detection of potato tubers using an ultraviolet imaging-based machine vision system. Biosystems Engineering. 105(2). 257–265. 32 indexed citations
20.
Al-Mallahi, Ahmad, et al.. (2008). An algorithm for Distinguishing Potato Tubers on the Conveyor of the Potato Harvester using UV Camera. 2008 Providence, Rhode Island, June 29 - July 2, 2008. 4 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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