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CoLeaf-DB:秘鲁咖啡叶图像数据集用于咖啡叶营养缺陷检测和分类

Victor A Tuesta-Monteza¹, Heber I Mejia-Cabrera¹, Juan Arcila-Diaz¹

¹Facultad de Ingeniería Arquitectura y Urbanismo, Universidad Señor de Sipán, Perú.

|June 29, 2023

概括

本研究介绍了CoLeaf数据集,其中包括1006张秘鲁咖啡叶的图像,这些咖啡叶表现出各种营养缺陷. 本资源有助于训练深度学习模型,以准确识别咖啡植物的健康状况.

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:

营养不足会对咖啡植物的产量和质量产生重大影响.
准确识别这些缺陷对于有效的作物管理至关重要.
现有的咖啡叶分析数据集有限,特别是在各种营养缺陷方面.

研究的目的:

介绍和描述CoLeaf数据集,这是一个新的咖啡叶图像集合.
为培训和验证咖啡植物营养缺陷分类的深度学习模型提供资源.
促进对咖啡种植的自动植物健康监测的研究.

主要方法:

农学家在秘鲁Jaén的咖啡种植园中发现营养不足.
设计用于图像捕获的受控环境.
采集了1006张咖啡叶的高分辨率数字图像 (品种:CATIMOR,CATURRA,BORBON).

主要成果:

CoLeaf数据集包括1006张图像,按特定营养缺乏症 (,铁,,,,,,,,,,和其他物质) 分类.
数据集的结构支持深度学习算法的培训和验证.
图像是在受控条件下拍摄的,以确保一致性和质量.

结论:

CoLeaf数据集是一个有价值的,公开可用的资源,用于推进自动化咖啡植物营养状况评估.
这一数据集可以加速开发人工智能驱动的工具,用于咖啡种植的精准农业.
进一步的研究可以利用CoLeaf来改善疾病和缺陷检测模型.

关键词:

深度学习是一种深度学习.缺陷检测发现缺陷发现缺陷图像数据集是一组图像数据集.机器学习是机器学习.秘鲁咖啡:秘鲁咖啡是一种咖啡.

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