综合智能手机图像数据集用于从孟加拉国蔬菜田中检测豆和牛植物叶病和评估新鲜度
Mahamudul Hasan1, Raiyan Gani1, Mohammad Rifat Ahmmad Rashid1
1Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Data in brief
|November 7, 2024
概括
在孟加拉国,早期发现植物疾病对于农业和粮食安全至关重要. 一个新的数据集由4467张叶子图像组成,有助于研究人员和农民识别豆类和牛植物疾病.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算机视觉 计算机视觉
背景情况:
- 农业对孟加拉国经济至关重要,蔬菜种植对于营养和粮食安全至关重要.
- 人口增长需要增加粮食生产,但农民由于植物疾病和缺乏技术解决方案而面临重大损失.
- 植物疾病的早期检测对于减轻经济损失和确保粮食安全至关重要.
研究的目的:
- 为了应对孟加拉国植物疾病检测的挑战.
- 引入一个全面的数据集,用于识别豆类和牛植物的疾病.
- 支持研究人员和农民在早期疾病识别和管理.
主要方法:
- 开发一个数据集,包括 4467 张豆和豆叶的图像.
- 该数据集包括健康叶子的图像和每个植物类型的四个不同的疾病类别.
- 数据收集集中在常见植物疾病的视觉识别上.
主要成果:
- 该数据集包含2,273张豆植物图像和2,194张牛植物图像.
- 每种植物类别都以健康的叶子和多种受疾病影响的叶子图像表示.
- 该数据集为培训和验证植物疾病检测模型提供了宝贵的资源.
结论:
- 创建的数据集是孟加拉国植物病理学和农业技术领域的重要贡献.
- 它将为研究人员和农民提供早期和准确的植物疾病诊断工具.
- 促进早期检测将有助于减少作物损失,提高农业生产率,并支持国民经济.
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