URT-YOLOv11: A Large Receptive Field Algorithm for Detecting Tomato Ripening Under Different Field Conditions

Di Mu, Yuping Guou, Wei Wang, Ran Peng, Chunjie Guo, Francesco Marinello, Yingjie Xie and Qiang Huang ()
Additional contact information
Di Mu: College of Information Engineering, Sichuan Agricultural University, Yaan 625000, China
Yuping Guou: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan 625000, China
Wei Wang: College of Information, SiChuan Finance And Economics Vocational College, Chengdu 610100, China
Ran Peng: College of Information Engineering, Sichuan Agricultural University, Yaan 625000, China
Chunjie Guo: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan 625000, China
Francesco Marinello: Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, Italy
Yingjie Xie: College of Information Engineering, Sichuan Agricultural University, Yaan 625000, China
Qiang Huang: College of Information Engineering, Sichuan Agricultural University, Yaan 625000, China

Agriculture, 2025, vol. 15, issue 10, 1-29

Abstract: This study proposes an improved YOLOv11 model to address the limitations of traditional tomato recognition algorithms in complex agricultural environments, such as lighting changes, occlusion, scale variations, and complex backgrounds. These factors often hinder accurate feature extraction, leading to recognition errors and reduced computational efficiency. To overcome these challenges, the model integrates several architectural enhancements. First, the UniRepLKNet block replaces the C3k2 module in the standard network, improving computational efficiency, expanding the receptive field, and enhancing multi-scale target recognition. Second, the RFCBAMConv module in the neck integrates channel and spatial attention mechanisms, boosting small-object detection and robustness under varying lighting conditions. Finally, the TADDH module optimizes the detection head by balancing classification and regression tasks through task alignment strategies, further improving detection accuracy across different target scales. Ablation experiments confirm the contribution of each module to overall performance improvement. Our experimental results demonstrate that the proposed model exhibits enhanced stability under special conditions, such as similar backgrounds, lighting variations, and object occlusion, while significantly improving both accuracy and computational efficiency. The model achieves an accuracy of 85.4%, recall of 80.3%, and m A P @ 50 of 87.3%. Compared to the baseline YOLOv11, the improved model increases m A P @ 50 by 2.2% while reducing parameters to 2.16 M, making it well-suited for real-time applications in resource-constrained environments. This study provides an efficient and practical solution for intelligent agriculture, enhancing real-time tomato detection and laying a solid foundation for future crop monitoring systems.

Keywords: maturity grading; complex environment; object detection; tomato (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/10/1060/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/10/1060/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:10:p:1060-:d:1655609

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().