Object Detection Algorithm for Citrus Fruits Based on Improved YOLOv5 Model

Yao Yu, Yucheng Liu, Yuanjiang Li, Changsu Xu and Yunwu Li ()
Additional contact information
Yao Yu: Guizhou Mountainous Agriculture Machinery Research Institute, Guiyang 550000, China
Yucheng Liu: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Yuanjiang Li: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Changsu Xu: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Yunwu Li: College of Engineering and Technology, Southwest University, Chongqing 400715, China

Agriculture, 2024, vol. 14, issue 10, 1-24

Abstract: To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture.

Keywords: fruit recognition; YOLOv5; weighted convolution; linear attention; clustering optimization (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/10/1798/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/10/1798/ (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:14:y:2024:i:10:p:1798-:d:1497600

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 ().