DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images

Dongbin Liu, Jiandong Fang () and Yudong Zhao
Additional contact information
Dongbin Liu: College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China
Jiandong Fang: College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China
Yudong Zhao: Inner Mongolia Key Laboratory of Intelligent Perception and System Engineering, Hohhot 010080, China

Agriculture, 2025, vol. 15, issue 12, 1-23

Abstract: Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), m A P 50 , and m A P 50 : 95 increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops.

Keywords: UAV; maize tassel; YOLOv8; deep learning; multi-scale target; target detection (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: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/12/1259/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/12/1259/ (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:12:p:1259-:d:1676412

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 ().