虫害数据集支持智能农业系统的建设
Luyl-Da Quach1, Quoc Khang Nguyen1, Quynh Anh Nguyen1
1FPT University, Can Tho campus, Cantho city, Vietnam.
Data in brief
|February 1, 2024
概括
这项研究提供了高质量的,经过专家验证的数据集,用于米疾病的检测和分类. 该资源支持智能农业和自动化系统,以改善全球大米种植.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 米是全球至关重要的粮食主食,特别是在亚洲,非洲和拉丁美洲.
- 病虫害严重影响米生产,威胁粮食安全和经济稳定.
- 现有的数据集可能缺乏多样性,阻碍了强大的自动检测系统的开发.
研究的目的:
- 引入一个全面的,经过专家验证的数据集,用于识别病.
- 促进开发自动化系统,用于在米种植中进行智能农业.
- 解决当前农业数据集数据多样性所带来的挑战.
主要方法:
- 来自不同来源的数据集编译,以确保大米疾病类型和视觉特征的多样性.
- 严格的专家审查和验证所有包含的数据.
- 数据预处理和统计分析,以提高数据集的质量和效用.
主要成果:
- 一个高质量的,免费可用的数据集,用于检测和分类大米疾病.
- 该数据集适合用于用于自动疾病识别的机器学习模型的训练.
- 识别了通过处理和分析不断增强数据的需求.
结论:
- 提供的数据集是推动农业自动化和智能农业技术的宝贵资源.
- 这一倡议支持用于种植大米的精准农业的研究和开发.
- 数据集的质量和可访问性旨在加速农作物疾病管理方面的创新.
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