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走向基于深度学习的智能农业,用于农作物的智能杂草管理
Muhammad Ali Saqib1, Muhammad Aqib1,2, Muhammad Naveed Tahir3,4
1University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan.
Frontiers in plant science
|August 14, 2023
概括
这项研究引入了深度学习杂草检测模型用于农业. YOLOv4模型在识别杂草方面取得了98.88%的准确性,提高了作物生产效率.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 杂草管理对于作物生产至关重要,而传统方法是劳动密集型的.
- 深度学习 (DL) 为包括农业在内的各种应用中自动检测对象提供了先进的解决方案.
研究的目的:
- 提出一个基于DL的模型,用于在作物中有效检测杂草.
- 为了评估不同You Only Look Once (YOLO) 杂草识别模型的性能.
主要方法:
- 使用基于卷积神经网络 (CNN) 的物体检测系统,你只看一次 (YOLO).
- 在四种杂草物种的数据集上训练了四个YOLO模型 (v3,v3-tiny,v4,v4-tiny).
- 应用LAB和HSV图像转换技术到数据集中.
主要成果:
- YOLOv4模型展示了最高的准确性,正确预测了98.88%的杂草.
- 实现了平均损失1.8和73.1%的平均精度.
- 图像转换 (LAB,HSV) 对模型性能的影响最小.
结论:
- 拟议的DL模型,特别是YOLOv4,对于杂草检测非常有效.
- 这项技术可以显著改善农业中的杂草管理策略.
- 未来与可变速率喷雾器的整合旨在实现实时,精确的杂草控制.
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