Mrtic Det: A structure aware detection framework for thinning stage fruit in non structured orchards

Yan, Jingyu, Duan, Huichuan, Xu, Rongfeng, Sun, Meili, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902, Xu, Li and Jia, Weikuan 2025. Mrtic Det: A structure aware detection framework for thinning stage fruit in non structured orchards. Computers and Electronics in Agriculture 239 (Part A) , 110940. 10.1016/j.compag.2025.110940 Item availability restricted.

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Abstract

The thinning period in orchards poses significant challenges, including small object detection, occlusions, dense distributions, and size variations. To address these issues, this study proposes MRtic-Det, an advanced object detection model designed to enhance accuracy and efficiency in fruit detection tasks. Built on the RT-DETR-L architecture, MRtic-Det incorporates the MODMamba backbone for superior feature extraction and the CrossSourceMerge Neck to improve multi-scale information fusion by integrating high-level spatial features with low-level visual cues. Additionally, a P2 layer detection head is introduced to strengthen small-object detection capabilities. The performance of MRtic-Det is evaluated on two self-collected datasets, including an apple thinning dataset and a golden pear thinning dataset. Experimental results demonstrate significant improvements, with MRtic-Det achieving an AP50 increase of 4.9 percentage points and an AP50-95 increase of 5.8 percentage points on the apple thinning dataset, while reducing model parameters by 45.4 %. The golden pear thinning dataset further validates the model’s generalization capability, underscoring its adaptability to various fruit types and orchard environments. MRtic-Det offers a robust and efficient solution for fruit thinning robots, advancing the field of precision agriculture.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0168-1699
Date of First Compliant Deposit:	1 October 2025
Date of Acceptance:	26 August 2025
Last Modified:	01 Oct 2025 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/181089

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