自动化杂草监测和控制:使用YOLOv7-AlexNet融合网络提高检测准确度
Muhammad Faizan Zeb1, Abid Iqbal2, Ghassan Husnain3
1Department of Computer Science, Iqra National University Peshawar, Peshawar, Pakistan.
Frontiers in plant science
|December 31, 2025
概括
结合YOLOv7用于检测和AlexNet用于分类的新混合深度学习系统,改善了农业杂草管理. 这种先进的系统提供了强大的实时杂草识别,提高了作物产量保护.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 深度学习是一种深度学习.
背景情况:
- 杂草入侵大大降低了作物产量,对全球粮食可持续性构成威胁.
- 当前的计算机视觉杂草检测方法难以区分相似的作物和杂草,不一致的性能和操作限制.
- 像YOLOv5,ResNet和Faster R-CNN这样的现有模型在准确性,速度和在密集的植被中检测小杂草方面存在局限性.
研究的目的:
- 开发一种混合深度学习系统,以提高农业中的杂草检测和物种分类.
- 克服现有的杂草检测技术的局限性,特别是在作物和杂草之间进行区分.
- 提高自动化杂草管理系统的准确性,效率和实时功能.
主要方法:
- 开发了一种混合深度学习方法,集成YOLOv7用于快速杂草检测和AlexNet用于精确的杂草物种分类.
- 选择YOLOv7是因为其优越的识别速度和在密集的环境中区分杂草的能力.
- 亚历克斯网被用来通过准确的杂草物种识别来增强系统特异性.
主要成果:
- 该YOLOv7-AlexNet混合型号实现了高性能指标:精度 (0.80),回忆 (0.85),F1得分 (0.87),mAP@0.50 (0.89),和mAP@0.5:0.95 (0.50).
- 实地测试证明了该系统的有效性,AlexNet在杂草物种分类方面实现了95%的精度,97%的回忆率和94%的F1分数.
- 混合模型在准确性,速度和在具有挑战性的农业环境中检测杂草方面明显优于以前的方法.
结论:
- YOLOv7-AlexNet混合模型为农业中实时杂草检测和分类提供了强大而高效的解决方案.
- 该系统增强了自动杂草管理的潜力,有助于提高作物产量和可持续农业实践.
- 未来的工作包括扩大数据集和在现场条件下部署模型以实现实际应用.
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