使用YOLOv8s快速检测大豆营养不足,以促进精准农业的发展
Minsoo Jeong1, Sihyun Park2, Sook-Min Kwon2
1Department of Applied Biosciences, Kyungpook National University, Daegu, 41566, Republic of Korea.
Scientific reports
|April 21, 2025
概括
这项研究使用YOLOv8s模型来检测大豆植物中,和的缺乏. 人工智能模型实现了高精度,使得可持续农业的早期检测成为可能.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 营养素缺乏会对作物产量和农业可持续性产生重大影响.
- 早期和准确地检测营养压力对于有效的作物管理至关重要.
研究的目的:
- 应用YOLOv8s物体检测模型来识别大豆植物中的,和缺乏.
- 评估模型的性能和适用于实时精准农业应用的适用性.
主要方法:
- 采用了一个独特的数据集,包括6020张缺乏营养素的大豆叶的图像.
- 在这个数据集上训练和验证了YOLOv8s对象检测模型.
- 模型性能被评估使用指标,如平均平均精度 (mAP@0.5) 和精度率.
主要成果:
- YOLOv8s模型实现了高水平的培训 (99.18% mAP@0.5) 和验证 (98.51% mAP@0.5) 性能.
- 对于个体营养缺乏的准确率从90.03%到96.54%不等,可检测出色的缺乏.
- 该模型快速处理图像 (3.46毫秒/图像),证明其适合实时现场使用.
结论:
- YOLOv8s模型提供了一个快速,准确和可扩展的解决方案,用于早期检测大豆作物中的营养缺乏.
- 这项技术有可能彻底改变肥料管理,提高可持续农业实践.
- 这项研究为精准农业领域做出了重大贡献.
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