全面的瓜果数据集:用于分析和分类的数字和热图像
P Pathmanaban1, B K Gnanavel2, Shanmuga Sundaram Anandan3
1Department of Automobile Engineering, Easwari Engineering College, Chennai, Tamil Nadu-600066, India.
Data in brief
|August 28, 2023
概括
这项研究引入了一套用于使用计算机视觉早期检测瓜病的数据集. 这项技术有助于农民识别感染,提高作物产量,防止瓜种植造成经济损失.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 瓜 (Psidium guajava) 产量受到感染和疾病导致产量下降的威胁.
- 早期发现瓜疾病对于减轻经济损失和确保可持续农业实践至关重要.
研究的目的:
- 提供一个全面的数据集,用于早期识别瓜疾病.
- 促进专家系统的开发,以便农民及时诊断疾病.
主要方法:
- 数据集包括瓜果的数字和热图像,展示了健康,受损和患病的情况 (病,病,病,腐烂).
- 图像按成熟度级别分类,并在各种下降高度下捕获,并包含有关损坏诱导,存储和捕获条件的详细信息.
- 用受控的热空气应用获得热图像.
主要成果:
- 该数据集为训练用于识别瓜病的机器学习模型提供了丰富的资源.
- 允许开发先进的图像处理技术,以区分健康和患病的瓜果.
- 根据视觉和热特性,促进不同类型的疾病的区分.
结论:
- 开发的数据集对于在瓜种植中推进自动化疾病检测系统至关重要.
- 这种技术方法支持可持续农业,通过及时干预水果疾病.
- 为农民提供早期疾病识别工具,从而改善作物管理和减少经济影响.
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