Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Crop yield prediction using machine learning: A systematic literature review
2020991 citationsAyalew Kassahun, Cagatay Catal et al.Computers and Electronics in Agricultureprofile →
Plant disease detection using drones in precision agriculture
202389 citationsCagatay Catal, Ayalew Kassahun et al.Precision Agricultureprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Ayalew Kassahun
Since
Specialization
Citations
This map shows the geographic impact of Ayalew Kassahun'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 Ayalew Kassahun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ayalew Kassahun more than expected).
This network shows the impact of papers produced by Ayalew Kassahun. 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 Ayalew Kassahun. The network helps show where Ayalew Kassahun may publish in the future.
Co-authorship network of co-authors of Ayalew Kassahun
This figure shows the co-authorship network connecting the top 25 collaborators of Ayalew Kassahun.
A scholar is included among the top collaborators of Ayalew Kassahun 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 Ayalew Kassahun. Ayalew Kassahun is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kassahun, Ayalew, et al.. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture. 177. 105709–105709.991 indexed citations breakdown →
Kassahun, Ayalew, Ioannis N. Athanasiadis, Andrea Emilio Rizzoli, et al.. (2010). Towards a service-oriented e-infrastructure for multidisciplinary environmental research. Socio-Environmental Systems Modeling.3 indexed citations
16.
Hårtog, R.J.M., et al.. (2008). Towards Synergy between Learning Management Systems and Educational Server Applications. Socio-Environmental Systems Modeling. 2008(1). 2247–2252.1 indexed citations
17.
Kassahun, Ayalew, Adrie Beulens, & R.J.M. Hårtog. (2006). Providing Author-Defined State Data Storage to Learning Objects. Educational Technology & Society. 9(2). 19–32.4 indexed citations
18.
Kassahun, Ayalew & H. Schölten. (2006). A knowledge base system for multidisciplinary model-based water management, Summit on Environmentel Modelling and Software, 3rd Biennial meeting of the International Environmental Modelling and Software Society, Burlington, Vermont, USA, July 9-12, 2006. Data Archiving and Networked Services (DANS).2 indexed citations
19.
Kassahun, Ayalew, et al.. (2005). Support for model based water management with the HarmoniQuA toolbox. Socio-Environmental Systems Modeling. 1282–1287.3 indexed citations
20.
Schölten, H., Ayalew Kassahun, & Jens Christian Refsgaard. (2004). Structuring multidisciplinary knowledge for model-based water management: the HarmoniQuA approach. Socio-Environmental Systems Modeling. 55–59.6 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.