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为了提高农业质量和市场准备性,RGB图像数据集用于的成熟度分类

Nikhilesh Kumar Bharathi¹, Ferbin Fj¹, Shoba S²

¹School of Mechanical Engineering, Vellore Institute of Technology, Kelambakkam - Vandalur Road, Chennai 600127, India.

|September 2, 2025

概括

使用图像分析对的成熟度进行分类有助于农民优化收获时间并减少浪费. 这种非破坏性的方法确保了消费者更好的品质和更好的市场结果.

科学领域:

农业科学
计算机视觉
食品科学

背景情况:

是一种营养丰富的作物, 对于消除营养不良和支持可持续农业至关重要.
对于收获时间,市场需求和用途来说,准确地分类的成熟度 (不成熟,成熟,过度成熟) 是至关重要的.
尽量减少收获后损失和优化用途取决于精确的成熟度评估.

研究的目的:

开发一种非破坏性方法来分类的成熟阶段.
创建来自不同现实环境的样图像数据集.
为优化农业实践和市场准备提供精确的分类模型.

主要方法:

采用非破坏性方法进行的成熟度分类.
通过使用标准的RGB摄像头在受控照明下收集了的图像.
确保数据集能够捕捉到不同农场和市场的自然变化.

主要成果:

开发了一种基于图像数据的成熟度分类方法.
这一庞大的数据集有助于创建精确的分类模型.
这种方法可以帮助农民优化收获时间,满足消费者的需求.

结论:

基于图像的非破坏性分类对于确定的成熟度是有效的.
这项研究加强了农业质量评估和市场准备.
通过优化收获促进可持续农业实践.

关键词:

进行分类资金成熟度水平乌克兰表型化

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