花叶病:用于智能农业的计算机视觉数据集
Sabbir Hossain Durjoy1, Md Emon Shikder1, Md Mehedi Hasan Shoib1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Data in brief
|June 19, 2025
概括
一个新的数据集,包括 2,661 张花叶片图像,有助于早期发现疾病. 本资源支持开发用于精准农业的机器学习模型,提高作物产量和减少农药使用.
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:
- 花叶病很难在早期诊断,导致快速传播和大量的作物损失.
- 目前的疾病管理往往依赖于广泛使用农药,这是昂贵和对环境有害的.
- 准确和及时的疾病诊断对于有效的作物管理和可持续农业至关重要.
研究的目的:
- 引入一套全面的白菜叶图像数据集,以加快检测植物疾病的研究.
- 促进开发先进的机器学习模型,用于早期识别和监测花花疾病.
- 通过加强疾病诊断,支持精准农业技术的进步.
主要方法:
- 收集了2,661张孟加拉国不同地点,天气条件和捕捉设备上的花菜叶的图像.
- 将图像分为三个类别:健康,昆虫洞和黑色腐烂.
- 通过调整尺寸 (3000x3000像素),调整亮度和删除重复/低质量样本来预处理图像,以确保培训准备.
主要成果:
- 一个高质量,多样化的数据集,准备用于训练深度学习模型,用于识别白菜叶病.
- 该数据集使得能够开发用于早期疾病检测的准确模型,超越了手动诊断的局限性.
- 实时应用程序的潜力,包括移动应用程序和智能农业设备集成,以便农民立即采取行动.
结论:
- 引入的数据集是农业研究的宝贵资源,特别是在精准农业和疾病管理方面.
- 它将对开发高度准确的机器学习模型,用于自动化监控和智能决策做出重大贡献.
- 该数据集支持实用,实时的疾病识别,为农民提供可操作的见解,减少对专家知识的依赖.
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