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teaLeafBD: 一个全面的图像数据集来分类患病的茶叶,以自动化孟加拉国的叶子选择过程

B M Shahria Alam¹, Fahad Ahammed¹, Golam Kibria¹

¹Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, Bangladesh.

|July 4, 2025

概括

一个包含5278张茶叶图像的新数据集有助于识别六种疾病和健康的叶子. 本资源支持开发人工智能模型,用于茶叶种植中的早期疾病检测.

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:

茶叶病严重影响作物产量和质量,对生产者造成财务损失.
准确识别茶叶疾病对于及时干预和有效管理至关重要.

研究的目的:

引入一套综合的病状和健康茶叶数据集,以推进植物病理学研究.
促进人工智能 (AI) 模型的开发,用于自动化茶叶疾病分类.

主要方法:

在不同的地理和气象条件下收集了5278张高分辨率图像的数据集.
图像被精心准备和分类,包括六种特定的茶叶疾病和健康样本.
该数据集旨在训练人工智能模型,特别是深度学习技术,用于疾病识别.

主要成果:

该数据集包含六种不同的茶叶疾病的图像:茶藻叶斑,棕色斑,灰色斑,黑龙斑,红色蜘蛛和绿色虫虫.
还包括一个代表健康茶叶的类别,为分类任务提供均衡的数据集.
数据集的高质量和多样性增强了它在开发强大的AI诊断工具方面的实用性.

结论:

这一数据集是训练人工智能模型准确分类茶叶疾病的宝贵资源.
自动化疾病分类系统可以使茶庄经理能够及时实施控制措施,减轻作物损失.
该数据集的可用性将大大有助于改善茶叶种植实践并确保更高的产量.

关键词:

农业农业农业农业人工智能的人工智能是人工智能.图像的分类图像的分类.图像数据集是一组图像数据集.植物疾病植物疾病茶叶病是一种茶叶病.

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