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HierbaNetV1:一个新的特征提取框架,用于基于深度学习的杂草识别

Justina Michael¹, Thenmozhi Manivasagam²

¹Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.

PeerJ. Computer science

|December 9, 2024

概括

本研究介绍了HierbaNetV1,一个新的卷积神经网络 (CNN) 框架,用于有效的图像特征提取. HierbaNetV1在作物杂草分类中达到98.06%的准确性,展示了卓越的性能和概括能力.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
农业技术农业技术

背景情况:

卷积神经网络 (CNN) 在特征提取和模式学习方面表现出色.
不同的兴趣区域 (ROI) 尺寸在图像分析中带来了挑战.
有效地区分作物和杂草对于农业生产率至关重要.

研究的目的:

提出HierbaNetV1,一个新的CNN框架,用于从图像中进行强大的特征提取.
用多元化过器来应对不同ROI大小的挑战.
开发一个准确和可通用的作物杂草分类模型.

主要方法:

开发了HierbaNetV1,一个特征提取框架,每个样本生成3872个特征地图.
集成的低级和高级特征,用于密集和多样化的学习.
创建并使用SorghumWeedDataset_Classification用于培训和测试.

主要成果:

在 SorghumWeedDataset_Classification 上,HierbaNetV1 的分类准确率达到了 98.06%.
该框架的性能优于预先训练的模型和最先进的架构.
废除研究和组件分析证实了HierbaNetV1在各种数据集中的有效性和概括性.

结论:

HierbaNetV1为作物杂草的分类提供了一个高度准确和有效的解决方案.
该模型表现出强大的概括能力,适用于各种作物和杂草.
这项研究通过HierbaApp和开源代码促进了社区进步的实际应用.

关键词:

分类分类分类分类.卷积神经网络 (CNN) 是一种神经网络.农作物杂草的识别方法功能提取功能提取功能集成功能集成功能.在 HierbaNetV1V1中使用.高级特征提取高层特征提取低级特征提取低级特征提取精准农业精准农业精准农业草杂草的数据集_分类

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