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新鲜日果的保质期估计TinyML-传感器

Ramasamy Srinivasagan¹, Maged Mohammed^2,3, Ali Alzahrani¹

¹Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al Hofuf 36362, Saudi Arabia.

Sensors (Basel, Switzerland)

|August 26, 2023

概括

这项研究使用机器学习和低成本传感器来预测新鲜日期的保质期. 修改大气的包装和在5°C的真空密封显著延长了保质期,为生产商提供了具有成本效益的解决方案.

科学领域:

农业科学农业科学
食品科学食品科学食品科学
数据科学数据科学数据科学

背景情况:

新鲜枣的保质期有限,导致坏和经济损失.
准确的保质期估计对于生产商,供应商和消费者来说至关重要.
修改大气包装 (MAP) 可以通过减缓成熟延长新鲜枣的保质期.

研究的目的:

应用快速,经济有效,非破坏性机器学习 (ML) 技术来预测新鲜日期的保质期.
为了优化储存条件,最大限度地提高新鲜枣的保质期.
为了能够及时消费,并保持枣的营养价值.

主要方法:

使用可见近红外 (VisNIR) 光谱传感器来捕获内部物理化学属性.
采用TinyML兼容的回归模型来预测水果发育阶段和估计保质期.
在真空下存储日期和MAP (20%CO2,N平衡) 在5°C和24°C,监测属性每周.

主要成果:

在5°C的真空包装将保质期延长到53天;在5°C的MAP将保质期延长到44天.
在24°C时,真空包装的保质期为44天,MAP的保质期为23天.
从Khalal过渡到Rutab阶段标志着保质期开始减少.

结论:

机器学习和VisNIR传感器提供了一种非破坏性的方法,用于实时估计新鲜日期的保质期.
低温储存 (5°C) 显著提高了真空和MAP在延长日期保质期方面的有效性.
边缘冲动方便部署TinyML模型用于实用,低成本的保质期预测应用程序.

关键词:

短波近红外近红外短波近红外在TinyML中使用TinyML.人工智能 (AI) 是一种人工智能.边缘计算是一种边缘计算.食品供应链中的食物供应链.改变了大气层的变化.预测模型预测模型回归模型是一种回归模型.

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