卷积神经网络基于图像分类和细分模块的融合,用于在草中检测杂草
Jie Yang1,2, Yong Chen1, Jialin Yu2
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Pest management science
|February 1, 2024
概括
一个新的ResNet-101细分模型显著改善了草田的实时宽叶杂草检测. 这一进步对于提高自主精密除草剂应用精度至关重要.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 实时杂草检测对于自主精确除草剂应用至关重要.
- 提高广叶杂草在草等作物中识别准确度是一个关键挑战.
研究的目的:
- 开发一种新的神经网络架构,用于增强杂草检测.
- 特别是为了提高在种植中检测宽叶杂草的准确性.
主要方法:
- 通过整合分类和细分模块,创建了一个新的神经网络,ResNet-101细分.
- 该模型使用ResNet-101作为其骨干架构.
- 绩效与已经建立的网络 (如AlexNet,GoogLeNet,VGG16和ResNet-101.1) 相比进行了评估.
主要成果:
- 通过ResNet-101的细分,对关键的宽叶杂草实现了很高的检测准确度:卡罗来纳 (98.17%),草 (98.28%),草 (96.23%) 和速井 (98.06%).
- 新型网络在杂草检测率方面显著超过现有模型.
- 观察到具有足够训练数据的高混矩阵值 (> 90%).
结论:
- 与现有模型相比,ResNet-101细分证明了在中检测宽叶杂草的性能优于现有模型.
- 这种方法为提高杂草检测准确度提供了显著的进步,特别是当杂草与作物有形态相似性时.
- 开发的模型为精密农业应用提供了一个有前途的解决方案.
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