番茄叶数据集:用于多类疾病检测和分类的数据集
Ahmed Imtiaz1, Fahad Bin Islam Swapnil2, Syed Rayhan Masud1
1Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh.
Data in brief
|April 21, 2025
概括
这项研究引入了新的番茄叶图像数据集,以训练人工智能检测六种常见疾病. 早期检测有助于精准农业,提高作物产量.
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:
- 番茄种植对孟加拉国经济至关重要,但疾病威胁到作物产量.
- 早期发现番茄植物疾病对于有效管理和农民生计至关重要.
研究的目的:
- 创建和共享一套全面的西红叶图像数据集,用于自动诊断疾病.
- 促进机器学习模型的开发,以便在番茄植物中早期和准确地检测疾病.
主要方法:
- 收集了731张西红叶的高分辨率图像,展示了六种常见疾病 (早期闪,黑点,晚期闪,叶,细菌点,目标点) 和健康样本.
- 结构化了数据集,用于计算机视觉和机器学习应用.
主要成果:
- 一个公开可用的,分类的数据集,用于疾病识别的西红叶图像.
- 该数据集支持对人工智能驱动的疾病管理农业工具的研究.
结论:
- 该数据集加速了精准农业和人工智能作物保护方面的研究.
- 授权开发工具,以加强番茄疾病管理,提高产量和可持续性.
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