Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network: papers linking to it

Mendeley Data · Dataset · 2019 · data.mendeley.com/datasets/tywbtsjrjv

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network (https://data.mendeley.com/datasets/tywbtsjrjv), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

5 papers in the tracked journals link to the Mendeley Data dataset Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network in their Crossref records, the first in 2022; most of them (2) in 2022.
By category: AI (5); the journals with the most are Neural Computing and Applications (4) and Expert Systems with Applications (1).

By year#

20222026

Papers linking to Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network by publication year
Year	Papers
2022	2
2023	1
2025	1
2026	1

Journals#

Journals whose papers link to Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network most
Journal	Papers
Neural Computing and Applications	4
Expert Systems with Applications	1

Papers linking to Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-10-17	An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model	Expert Systems with Applications	Santosh Kumar Sahu, Manish Pandey	160	cites it
2022-07-04	Leaf species and disease classification using multiscale parallel deep CNN architecture	Neural Computing and Applications	Newlin Shebiah Russel, Arivazhagan Selvaraj	80	cites it
2023-03-28	EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction	Neural Computing and Applications	C Ashwini, V. Sellam	21	cites it
2025-04-09	An efficient deep learning model for early disease detection in vegetable crops	Neural Computing and Applications	Amit Bhola, Prabhat Kumar	1	cites it
2026-02-01	Smart green Guardian: leveraging transfer learning for early plant disease detection in kitchen gardens	Neural Computing and Applications	Megha Singh Chauhan, K. Srinivas, A. Charan Kumari	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/mendeley-tywbtsjrjv

@misc{scholar4ai-code-mendeley-tywbtsjrjv,
  author = {{Scholar4AI}},
  title = {Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/mendeley-tywbtsjrjv}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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