eprintid: 17545
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/75/45
datestamp: 2023-12-19 03:23:55
lastmod: 2023-12-19 03:23:55
status_changed: 2023-12-19 03:08:14
type: article
metadata_visibility: show
creators_name: Aslam, F.
creators_name: Khan, Z.
creators_name: Tahir, A.
creators_name: Parveen, K.
creators_name: Albasheer, F.O.
creators_name: Ul Abrar, S.
creators_name: Khan, D.M.
title: A Survey of Deep Learning Methods for Fruit and Vegetable Detection and Yield Estimation
ispublished: pub
keywords: Computer vision; Deep learning; Fruits; Grading; Large dataset; Learning algorithms; Learning systems; Surveys; Vegetables, 'current; Deep learning; Detection estimation; Food industries; Fruit and vegetables; Fruit harvesting; Learning methods; Objects detection; Tools and techniques; Yield estimation, Object detection
note: cited By 6
abstract: Computer vision has a great potential to deal with agriculture problems. It is crucial to utilize novel tools and techniques in the agriculture food industry. The focus of current studies is to automate the fruit harvesting, grading of fruits, fruit recognition, and identification of diseases in the agriculture domain using deep learning and computer vision. Integrating deep learning with computer vision facilitates the consistent, speedy and trustworthy classification of fruit and vegetables compared to the traditional machine learning algorithm. However, there are still some challenges, such as the need for expert farmers to develop large-scale datasets to recognize and identify the problems of agriculture production. This survey includes eighty papers relevant to deep learning and computer vision techniques in the agriculture field. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137578486&doi=10.1007%2f978-3-031-05752-6_19&partnerID=40&md5=2b5ee8b69c85a5e80debea316ec22606
id_number: 10.1007/978-3-031-05752-6₁₉
full_text_status: none
publication: Studies in Big Data
volume: 111
pagerange: 299-323
refereed: TRUE
issn: 21976503
citation:   Aslam, F. and Khan, Z. and Tahir, A. and Parveen, K. and Albasheer, F.O. and Ul Abrar, S. and Khan, D.M.  (2022) A Survey of Deep Learning Methods for Fruit and Vegetable Detection and Yield Estimation.  Studies in Big Data, 111.  pp. 299-323.  ISSN 21976503