Evidence map›Paper›PMID 38067888›Full record

ArticleSensors (Basel, Switzerland)2023

EfficientRMT-Net-An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases.

Kashif Shaheed, Imran Qureshi, Fakhar Abbas, Sohail Jabbar, Qaisar Abbas, Hafsa Ahmad, Muhammad Zaheer Sajid

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
33.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 83 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 4 countries.

Kashif ShaheedDepartment of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.
Imran QureshiCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.ORCID 0000-0002-8542-7112
Fakhar AbbasCentre for Trusted Internet and Community, National University of Singapore (NUS), Singapore 117411, Singapore.
Sohail JabbarCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.ORCID 0000-0002-2127-1235
Qaisar AbbasCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.ORCID 0000-0002-0361-1363
Hafsa AhmadDepartment of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan.
Muhammad Zaheer SajidDepartment of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan.ORCID 0009-0005-5794-7902
Imam Mohammad ibn Saud Islamic University · SANational University of Sciences and Technology · PKGdańsk University of Technology · PLNational University of Singapore · SG

Funding

The Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University IMSIU-RP23063
6 · The paper itself

Abstract

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by

Indexed as

Solanum tuberosumAgricultureCrops, AgriculturalCulturePlant DiseasesPlant LeavesagricultureclassificationCNNsdeep learningpotato diseasesResNet-50transfer learning (TL)vision transformer (ViT)

Identifiers

PMID38067888
PMCPMC10708852
OpenAlexW4389193226

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.