ODL Net: Object detection and location network for small pears around the thinning period

Lu, Yuqi, Du, Shuang, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902, Yin, Xiang and Jia, Weikuan 2023. ODL Net: Object detection and location network for small pears around the thinning period. Computers and Electronics in Agriculture 212 , 108115. 10.1016/j.compag.2023.108115

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Abstract

In the process of efficient management of intelligent orchards, due to the short cycle and high intensity of fruit thinning, it is urgent to realize the automatic operation of fruit thinning in orchards. However, affected by the complex orchard environment, the color of fruit and the background are similar, and the more important problem is that the fruit is small-scale. These factors bring great challenges to fruit detection before and after the thinning period. For this reason, a detection algorithm for fruits of small green objects is proposed, namely, ODL Net. By integrating the semantic enhancement module and label assignment Center-Box, the small size problem of the target fruit is alleviated. The feature enhancement module and position enhancement module are constructed to enhance the fusion effect of features and improve the detection accuracy. To better verify the performance of the algorithm, this study takes a pear orchard as an example to produce two datasets before and after pear thinning. The experimental results show that the detection accuracy of ODL Net can reach 56.2% and 65.1% before and after the fruit thinning period, respectively, and the recall rate can reach 61.3% and 70.8%, respectively, which are significantly higher than those of other mainstream algorithms at present. The new algorithm can effectively assist the orchard automatic fruit thinning operation and provide the basis for orchard yield measurement after the fruit thinning period. This study can provide a theoretical basis for the scientific management of intelligent orchards.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0168-1699
Date of First Compliant Deposit:	23 November 2023
Date of Acceptance:	25 July 2023
Last Modified:	09 Nov 2024 06:00
URI:	https://orca.cardiff.ac.uk/id/eprint/163390

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