Edna C. Too

1.5k citations
11 papers · 960 · 1 hit paper · h-index 5

Impact in

    • Spectroscopy and Chemometric Analyses
    • Smart Agriculture and AI
    • Leaf Properties and Growth Measurement
    • Plant Disease Management Techniques
    • Date Palm Research Studies
    • Plant Virus Research Studies
    • Greenhouse Technology and Climate Control

Papers in

Edna C. Too

10 papers receiving 899 citations

Edna C. Too's Hit Papers

A comparative study of fine-tuning deep learning models for plant disease identification 2018 · 852 citations
8520+2+5Years since publication250500750

Peers

Edna C. Too
Comparison fields: 5 of 99
  • Analytical Chemistry 291
  • Plant Science 786
  • Ecology 168
  • Computer Vision and Pattern Recognition 65
  • Media Technology 21
Replace Mónica G. Larese with:
Mónica G. Larese Argentina
J. Arun Pandian India
Yuandong Sun China
Sue Han Lee Malaysia
Jinzhu Lu China
Muhammad Hammad Saleem New Zealand
Vinay Gautam India
Chad DeChant United States
V. K. Singh India
Shanwen Zhang China
Edna C. Too relative to Mónica G. Larese Argentina Mónica G. Larese's profile →
Citations per field
00.5×10×17×
Mónica G. Larese · 1×
Citations per year

Countries citing papers authored by Edna C. Too

Since Specialization
Citations

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

Fields of papers citing papers by Edna C. Too

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Edna C. Too. 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 Edna C. Too. The network helps show where Edna C. Too may publish in the future.

Co-authors

The 7 scholars most cited alongside Edna C. Too, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Edna C. Too Line = papers co-authored together Edna C. Too links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
A comparative study of fine-tuning deep learning models for plant disease identification
Hit paper breakdown →
2018852
2 202143
3 202123
4 201917
5 202017
6 20192
7 20242
8 20202
9 20231
10 20181
11 20200

About Edna C. Too

Edna C. Too is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Radiology, Nuclear Medicine and Imaging and Information Systems, having authored 11 papers that have together received 960 indexed citations. Recurring topics across this work include Smart Agriculture and AI (4 papers), Advanced Neural Network Applications (4 papers), Neural Networks and Applications (2 papers), COVID-19 diagnosis using AI (2 papers), Machine Learning and ELM (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Plant Disease Management Techniques (1 paper) and Date Palm Research Studies (1 paper). The work is most often cited by research in Analytical Chemistry (291 citations), Plant Science (786 citations), Ecology (168 citations), Computer Vision and Pattern Recognition (65 citations) and Media Technology (21 citations). Edna C. Too has collaborated with scholars based in Kenya and China. Frequent co-authors include Yujian Li, Yingchun Liu, Benson Kipkemboi Kenduiywo, Pius Kwao Gadosey, Zhaoying Liu, Jianbiao Zhang and Ting Zhang. Their work appears in journals such as Data in Brief, Computers and Electronics in Agriculture, Journal of Intelligent & Fuzzy Systems, International Journal of Computational Science and Engineering and International Journal of Advanced Computer Science and Applications.

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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