MoringaLeafNet:用于精准农业和深度学习研究的多类叶病数据集
Sabit Ahamed Preanto1, Tapon Paul1, Abid Khan1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Data in brief
|November 10, 2025
概括
一个新的莫林加树叶图像数据集有助于早期发现疾病. 本资源支持开发人工智能系统,以识别黄色叶子,细菌叶子斑点和Cercospora叶子斑点,改善作物产量和可持续性.
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:
- 在全球范围内,Moringa Oleifera因其营养和健康益处而受到重视.
- 由于黄叶,细菌叶斑和Cercospora叶斑等疾病的快速传播,Moringa种植面临着挑战.
- 疾病爆发会降低作物产量,通过使用农药增加农民成本,并损害环境.
研究的目的:
- 引入MoringaLeafNet数据集,用于早期检测Moringa叶病.
- 促进先进农业疾病诊断系统的发展.
- 为农民提供实时洞察力,以改善决策和作物管理.
主要方法:
- 收集了不同疾病类别 (健康的叶子,黄色的叶子,细菌叶子斑点,Cercospora叶子斑点) 的Moringa叶子的高质量图像.
- 从孟加拉国的两个托儿所在不同的天气条件下获得的图像.
- 应用数据增强技术,包括随机旋转,翻转和亮度/对比度调整,用于深度学习应用.
主要成果:
- MoringaLeafNet数据集包括分为四个不同的类别的图像.
- 数据增强确保了对深度学习模型培训的稳定性和适用性.
- 该数据集是开发Moringa疾病自动识别工具的基础资源.
结论:
- 莫林加叶网数据集对于促进早期识别莫林加叶病至关重要.
- 这一资源可以为开发人工智能农业诊断系统做出重大贡献.
- 通过及时检测疾病来赋予农民权力,可以提高作物产量和可持续的农业实践.
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