relation: https://khub.utp.edu.my/scholars/17545/
title: A Survey of Deep Learning Methods for Fruit and Vegetable Detection and Yield Estimation
creator: Aslam, F.
creator: Khan, Z.
creator: Tahir, A.
creator: Parveen, K.
creator: Albasheer, F.O.
creator: Ul Abrar, S.
creator: Khan, D.M.
description: 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.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   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     
relation: 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
relation: 10.1007/978-3-031-05752-6₁₉
identifier: 10.1007/978-3-031-05752-6₁₉