DECC-Net: A Maize Tassel Segmentation Model Based on UAV-Captured Imagery

Yinchuan Liu, Lili He, Yuying Cao, Xinyue Gao, Shoutian Dong () and Yinjiang Jia ()
Additional contact information
Yinchuan Liu: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Lili He: Department of Academic Theory Research, Northeast Agricultural University, Harbin 150030, China
Yuying Cao: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Xinyue Gao: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Shoutian Dong: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Yinjiang Jia: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China

Agriculture, 2025, vol. 15, issue 16, 1-21

Abstract: The male flower of the maize plant, known as the tassel, is a strong indicator of the growth, development, and reproductive stages of maize crops. Monitoring maize tassels under natural conditions is significant for maize breeding, management, and yield estimation. Unmanned aerial vehicle (UAV) remote sensing combined with deep learning-based semantic segmentation offers a novel approach for monitoring maize tassel phenotypic traits. The morphological and size variations in maize tassels, together with numerous similar interference factors in the farmland environment (such as leaf veins, female ears, etc.), pose significant challenges to the accurate segmentation of tassels. To address these challenges, we propose DECC-Net, a novel segmentation model designed to accurately extract maize tassels from complex farmland environments. DECC-Net integrates the Dynamic Kernel Feature Extraction (DKE) module to comprehensively capture semantic features of tassels, along with the Lightweight Channel Cross Transformer (LCCT) and Adaptive Feature Channel Enhancement (AFE) modules to guide effective fusion of multi-stage encoder features while mitigating semantic gaps. Experimental results demonstrate that DECC-Net achieves advanced performance, with IoU and Dice scores of 83.3% and 90.9%, respectively, outperforming existing segmentation models while exhibiting robust generalization across diverse scenarios. This work provides valuable insights for maize varietal selection, yield estimation, and field management operations.

Keywords: maize tassel; semantic segmentation; unmanned aerial vehicle; cross attention; deep learning (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/2077-0472/15/16/1751/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/16/1751/ (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:16:p:1751-:d:1725323

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 ().