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棉花叶图像数据集用于疾病分类和健康监测

Shamim Ripon¹, Raiyan Gani¹, Nazratan Mazumder Niha¹

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

|October 29, 2025

概括

这项研究引入了一个新的图像数据集,用于早期,自动检测棉花叶病. 该数据集通过启用机器学习模型来监测作物健康状况和可持续农业来帮助精准农业.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物病理学植物病理学

背景情况:

棉花是全球重要的作物,但叶子疾病显著降低了产量和质量.
早期和自动检测疾病对于有效的作物管理和可持续农业至关重要.

研究的目的:

为棉花叶病研究引入全面的高分辨率图像数据集.
促进机器学习模型的开发,用于自动识别疾病和作物健康监测.

主要方法:

策划了1373个原始和4963个增强图像的棉花叶的数据集.
在各种环境条件下拍摄的图像,包括健康,受损和受感染的样本.
包括四种常见的棉花叶病:Fusarium枯,Alternaria叶斑,Verticillium枯和细菌,所有这些都被标记为机器学习.

主要成果:

数据集提供了丰富的视觉内容,从各种角度和设备捕获的图像.
精心策划的数据集支持开发强大的深度学习模型,用于疾病分类.
能够研究早期疾病检测和持续的作物健康监测.

结论:

开发的图像数据集是推动棉花种植精准农业的宝贵资源.
通过改进疾病管理策略,促进对可持续棉花农业的研究.
支持开发用于实时监测和早期干预棉花植物疾病的自动化系统.

关键词:

农业的挑战农业的挑战棉花数据集棉花数据集棉花叶病是棉花叶病的一种疾病.数据集收集数据集的收集.疾病识别疾病识别图像分析图像分析叶子疾病叶子疾病

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