eprintid: 18025
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/80/25
datestamp: 2024-06-04 14:10:07
lastmod: 2024-06-04 14:10:07
status_changed: 2024-06-04 14:01:08
type: article
metadata_visibility: show
creators_name: Ahmed, S.U.
creators_name: Shuja, J.
creators_name: Tahir, M.A.
title: Leaf classification on Flavia dataset: A detailed review
ispublished: pub
keywords: Plants (botany), Botany; Computer scientists; Flavium dataset; Leaf classification; Plant classification; Plant species; Plant species recognition; Publicly accessible; Species classification; Species recognition, Classification (of information)
note: cited By 2
abstract: For decades, vision scientists have contemplated the topic of plant species classification. As plants are of great importance to medicinal research, they are utilized in a wide range of medications. Plants are required in a variety of ways in order to save the species from extinction and provide an abundance of food through agriculture. Therefore,Botanists and computer scientists must conduct extensive plant species research. The plant resources are necessary for the survival of the world's nations The purpose of this paper is to examine the frequently utilized and publicly accessible dataset for plant classification in the past. We explored over 200 research papers for a deep understanding of the area. Briefly described are the procedural advancements and developments in the field of leaf classification. All the major techniques with significant advancements, the new effective approaches, and the novel techniques are discussed in this research. For the benefit of future researchers, the findings, research gap and transition, and coherence of algorithms in terms of several measurements are underlined. The hundreds of publications on a single benchmark dataset illustrate the progression of the recognition process, improvements, and innovations. © 2023 Elsevier Inc.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170279553&doi=10.1016%2fj.suscom.2023.100907&partnerID=40&md5=e9656eac4e3d0b395e41abc924dff341
id_number: 10.1016/j.suscom.2023.100907
full_text_status: none
publication: Sustainable Computing: Informatics and Systems
volume: 40
refereed: TRUE
citation:   Ahmed, S.U. and Shuja, J. and Tahir, M.A.  (2023) Leaf classification on Flavia dataset: A detailed review.  Sustainable Computing: Informatics and Systems, 40.