在蒙特利县使用精选的RGB图像数据集对INSV相关杂草进行深度学习分类
Arun K Sharma1, Arun D Jani2, Elijah Brunnengraeber2
1Department of Biology, Agriculture, and Chemistry, California State University, Monterey Bay, Seaside, CA, 93955, USA. arsharma@csumb.edu.
Scientific reports
|November 26, 2025
概括
深度学习模型能够准确地识别诸如年草和小草等杂草,这对于防止在精准农业中因Impatiens Necrotic Spot Virus (INSV) 造成的作物损失至关重要.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 杂草因争夺资源和传播疾病而造成大量的作物损失.
- 桑丘斯 (Sonchus oleraceus) 和马尔瓦 (Malva parviflora) 与蒙特利县因不良人死斑病毒 (INSV) 造成的1.5亿美元损失有关.
- 精准农业需要先进的工具来早期检测杂草和疾病管理.
研究的目的:
- 为蒙特利县的INSV相关杂草开发一个特定区域的图像数据集.
- 评估深度学习模型对视觉上相似的杂草进行分类的性能.
- 在高价值作物生产中建立实时杂草检测系统的基础.
主要方法:
- 在受控温室条件下创建了Sonchus oleraceus和Malva parviflora的高分辨率图像数据集.
- 对比ResNet-50,ResNet-101和DenseNet-121卷积神经网络用于杂草分类.
- 利用数据增强和十个分层数据分割来进行强大的模型训练和验证.
主要成果:
- ResNet-101获得了最高的中位分类准确率 (91%) 和科恩的卡帕 (0.87).
- 在DenseNet-121中,F1得分和曲线下的面积 (AUC) 值 (>0.99) 呈现出优异的情况.
- 数据集增强显著改善了模型概括和分类性能.
结论:
- 深度学习模型对于准确的杂草识别是有效的,即使与视觉上相似的物种.
- 开发的数据集解决了加利福尼亚高价值作物系统的关键差距.
- 这项研究支持在精准农业中开发可持续的,有针对性的除草策略.
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