MSMT-RTDETR: A Multi-Scale Model for Detecting Maize Tassels in UAV Images with Complex Field Backgrounds

Zhenbin Zhu, Zhankai Gao, Jiajun Zhuang (), Dongchen Huang, Guogang Huang, Hansheng Wang, Jiawei Pei, Jingjing Zheng and Changyu Liu ()
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Zhenbin Zhu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zhankai Gao: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Jiajun Zhuang: Academy of Contemporary Agriculture Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Dongchen Huang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Guogang Huang: School of Oceanography, Shanwei Institute of Technology, Shanwei 516600, China
Hansheng Wang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Jiawei Pei: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Jingjing Zheng: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Changyu Liu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 15, 1-24

Abstract: Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision detection of maize tassels, including maize tassel multi-scale variations caused by varietal differences and growth stage variations, intra-class occlusion, and background interference. To achieve accurate maize tassel detection in UAV images under complex field backgrounds, this study proposes an MSMT-RTDETR detection model. The Faster-RPE Block is first designed to enhance multi-scale feature extraction while reducing model Params and FLOPs. To improve detection performance for multi-scale targets in complex field backgrounds, a Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) is constructed by upgrading the CCFM through dynamic sampling strategies and multi-branch architecture. Furthermore, the MPCC3 module is built via re-parameterization methods, and further strengthens cross-channel information extraction capability and model stability to deal with intra-class occlusion. Experimental results on the MTDC-UAV dataset demonstrate that the MSMT-RTDETR significantly outperforms the baseline in detecting maize tassels under complex field backgrounds, where a precision of 84.2% was achieved. Compared with Deformable DETR and YOLOv10m, improvements of 2.8% and 2.0% were achieved, respectively, in the mAP 50 for UAV images. This study proposes an innovative solution for accurate maize tassel detection, establishing a reliable technical foundation for maize yield estimation.

Keywords: maize tassel; multi-scale target; deep learning; object detection; detection transformer; phenotype (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
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