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Deep learning – Method overview and review of use for fruit detection and yield estimation

journal contribution
posted on 15.10.2019, 00:00 by Anand KoiralaAnand Koirala, Kerry WalshKerry Walsh, Zhenglin WangZhenglin Wang, C McCarthy
A review of developments in the rapidly developing field of deep learning is presented. Recommendations are made for original contributions to the literature, as opposed to formulaic applications of established methods to new application areas (e.g., to new crops), including the use of standard metrics (e.g., F1 score, the harmonic mean between Precision and Recall) for model comparison involving binary classification. A recommendation for the provision and use of publically available fruit-in-orchard image sets is made, to allow method comparisons and for implementation of transfer learning for deep learning models trained on the large public generic datasets. Emphasis is placed on practical aspects for application of deep learning models for the task of fruit detection and localisation, in support of tree crop load estimation. Approaches to the extrapolation of tree image counts to orchard yield estimation are also reviewed, dealing with the issue of occluded fruit in imaging. The review is intended to assist new users of deep learning image processing techniques, and to influence the direction of the coming body of application work on fruit detection. © 2019 Elsevier B.V.

Funding

Category 3 - Industry and Other Research Income

History

Volume

162

Start Page

219

End Page

234

Number of Pages

16

eISSN

1872-7107

ISSN

0168-1699

Publisher

Elsevier, Netherlands

Peer Reviewed

Yes

Open Access

No

Acceptance Date

13/04/2019

External Author Affiliations

University of Southern Queensland

Author Research Institute

Institute for Future Farming Systems

Era Eligible

Yes

Journal

Computers and Electronics in Agriculture