AG-WDS/MSQE

Maize Seedling Quality Evaluation with Oriented YOLO11-Mamba and UAV-RGB Images # MSQE # Maize Seedling Quality Evaluation with Oriented YOLO11-Mamba and UAV-RGB Images # ⚠ DATA and CODE Usage > [!IMPORTANT] > **Restrictions:** The shared dataset and code are restricted to validation and comparative analysis. Any use of this data for independent publications is prohibited in the absence of additional licensing or permissions. # Dataset The MSQE dataset is available at `drive.google.com` # Setup Preparing the Code git clone github.com cd MSQE/YOLO11-Mamba Install the mamba dependencies pip install ultralytics pip install causal-conv1d>=1.1.2 pip install mamba-ssm>=1.1.2 Requirement ``` python>=3.8.0 pytorch-cuda==11.3 torch==1.12.1 ``` # Citations @article {Minghu Zhao,2025, tilte={Evaluating maize emergence quality with multi-task YOLO11-Mamba and UAV-RGB remote sensing}, author={Minghu Zhao, Dashuai Wang*, Gan Zhang, Wujing Cao, Sheng Xu, Zhuolin Li, Xiaoguang Liu*}, journal={Smart Agricultural Technology}, doi={doi.org, volume={12}, pages={101351}, year={2025} } # Acknowledgement This repo is modified from open source real-time object detection codebase Ultralytics