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在不同的地形条件下改进了基于YOLOv8的烟草植物计数
Xixin Zhou1, Yingjian Lu2, Haiping Li3
1School of Chemistry and Materials Science, Hunan Agricultural University, Changsha, Hunan, China.
Science progress
|November 20, 2025
概括
这项研究介绍了YOLOv8-AKConv-MLCA,这是一种改进的AI模型,用于在无人机图像中准确的自动化烟草植物计数. 它提高了精度和回忆力,为智能农业提供了强大的解决方案.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 手动计数烟草植物是劳动密集型的,容易出现错误.
- 现有的自动化方法在复杂的现场条件下与功能损失和背景噪声作斗争.
- 准确的植物计数对于产量估计和优化种植密度至关重要.
研究的目的:
- 使用无人机图像开发一个增强的YOLOv8n框架,用于使用无人机图像进行准确的烟草植物计数.
- 提高在不同地形 (平原和山地) 中的检测准确性和稳定性.
- 为精准农业提供高效和自动化解决方案.
主要方法:
- 集成可变内核卷积 (AKConv) 模块用于自适应采样和多尺度特征表示.
- 整合混合本地频道注意 (MLCA) 模块,通过融合空间和频道依赖来增强特征学习.
- 在UAV数据集上进行了广泛的实验和验证,涵盖平原和山地场景.
主要成果:
- 实现了高计数精度:97.20% (平地) 和96.13% (山地).
- 在平均平均精度 (mAP) 方面表现优于基线YOLOv8和其他最先进的模型 (SSD,更快的R-CNN,YOLOv8-Trans).
- 保持了实时推断速度 (219-227 FPS),同时显著改善了检测指标.
结论:
- YOLOv8-AKConv-MLCA框架为自动化烟草植物计数提供了卓越的准确性和效率的权衡.
- 该模型在复杂的现场环境中展示了强大的概括能力.
- 这项技术通过可靠的自动化植物评估,支持精准种植和智能农业管理.
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