果数据集用于根据甜度,成熟度和品种进行分类
Shilpa Gaikwad1, Sonali Kothari1, Ignisha Rajathi G2
1Symbiosis Institute of Technology - Pune Campus, Symbiosis International (Deemed University), Pune, India.
Data in brief
|July 28, 2025
概括
一个新的多光谱成像系统为非破坏性果质量评估提供了一种具有成本效益的方法. 这个系统生成了一个大型的注释数据库,用于训练机器学习模型来分类成熟度,甜度和品种.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 对于食品工业和农业自动化来说,果实质量评估的非破坏性方法至关重要.
- 精确确定果的成熟度,甜度和品种对于质量控制和消费者满意度至关重要.
研究的目的:
- 开发一个具有成本效益的,自行设计的多光谱成像系统,用于非破坏性果质量检查.
- 创建一个详细的,多光谱图像的注释数据库用于训练机器学习算法.
- 为了使主要水果成分的分类,包括成熟度,糖含量 (Brix) 和品种.
主要方法:
- 使用自行设计的多光谱成像系统,捕获8个离散波段的图像.
- 严格的环境条件和可控制的照明确保了测量的一致性和可重复性.
- 收集了32463张多光谱图像的综合数据集,涵盖甜度,成熟度和品种分类.
- 图像被连接成适合深度学习应用程序的统一表示,包括AppleNet.
主要成果:
- 该系统成功获取了用于非破坏性确定果质量参数的光谱信息.
- 生成的数据库为监督学习分类器提供了定量参考点.
- 数据集包括甜度评估图像 (1620张图像,10-15%Brix),成熟度 (29,160张图像,18天周期) 和品种 (1683张图像,3种品种).
结论:
- 开发的多谱成像系统和相关数据库为推进非破坏性水果质量评估提供了宝贵的资源.
- 该方法可以将其广泛应用于其他水果和农产品,支持智能农业倡议.
- 这项工作有助于开发基于机器学习的算法,用于自动化水果质量评估和疾病检测.
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