Deep learning - method overview and review of use for fruit detection and yield estimation

Article


Koirala, Anand, Walsh, Kerry B., Wang, Zhenglin and McCarthy, Cheryl. 2019. "Deep learning - method overview and review of use for fruit detection and yield estimation." Computers and Electronics in Agriculture. 162, pp. 219-234. https://doi.org/10.1016/j.compag.2019.04.017
Article Title

Deep learning - method overview and review of use for fruit detection and yield estimation

ERA Journal ID41630
Article CategoryArticle
AuthorsKoirala, Anand (Author), Walsh, Kerry B. (Author), Wang, Zhenglin (Author) and McCarthy, Cheryl (Author)
Journal TitleComputers and Electronics in Agriculture
Journal Citation162, pp. 219-234
Number of Pages16
Year2019
PublisherElsevier
Place of PublicationNetherlands
ISSN0168-1699
Digital Object Identifier (DOI)https://doi.org/10.1016/j.compag.2019.04.017
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0168169919301164
Abstract

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 a real so 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.

Keywordsprecision horticulture, yield estimation, YOLO, machine vision
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020300806. Post harvest horticultural technologies (incl. transportation and storage)
401411. Packaging, storage and transportation (excl. food and agricultural products)
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Byline AffiliationsCentral Queensland University
National Centre for Engineering in Agriculture
Institution of OriginUniversity of Southern Queensland
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