U-MoEMamba: A Hybrid Expert Segmentation Model for Cabbage Heads in Complex UAV Low-Altitude Remote Sensing Scenarios

Rui Li, Xue Ding (), Shuangyun Peng and Fapeng Cai
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Rui Li: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Xue Ding: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Shuangyun Peng: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Fapeng Cai: Faculty of Geography, Yunnan Normal University, Kunming 650500, China

Agriculture, 2025, vol. 15, issue 16, 1-27

Abstract: To address the challenges of missed and incorrect segmentation in cabbage head detection under complex field conditions using UAV-based low-altitude remote sensing, this study proposes U-MoEMamba, an innovative dynamic state-space framework with a mixture-of-experts (MoE) collaborative segmentation network. The network constructs a dynamic multi-scale expert architecture, integrating three expert paradigms—multi-scale convolution, attention mechanisms, and Mamba pathways—for efficient and accurate segmentation. First, we design the MambaMoEFusion module, a collaborative expert fusion block that employs a lightweight gating network to dynamically integrate outputs from different experts, enabling adaptive selection and optimal feature aggregation. Second, we propose an MSCrossDualAttention module as an attention expert branch, leveraging a dual-path interactive attention mechanism to jointly extract shallow details and deep semantic information, effectively capturing the contextual features of cabbages. Third, the VSSBlock is incorporated as an expert pathway to model long-range dependencies via visual state-space representation. Evaluation on datasets of different cabbage growth stages shows that U-MoEMamba achieves an mIoU of 89.51% on the early-heading dataset, outperforming SegMamba and EfficientPyramidMamba by 3.91% and 1.4%, respectively. On the compact heading dataset, it reaches 91.88%, with improvements of 2.41% and 1.65%. This study provides a novel paradigm for intelligent monitoring of open-field crops.

Keywords: UAV-based low-altitude remote sensing; cabbage head segmentation; mixture of experts (MoE); attention mechanism; U-MoEMamba (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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