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Updated: May 24, 2025

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嵌入式系统的轻量级深度学习模型有效地预测了菜中的油脂和蛋白质含量
Mengshuai Guo1, Huifang Ma2, Xin Lv1
1Key Laboratory of Oilseeds Processing of Ministry of Agriculture, Hubei Key Laboratory of Lipid Chemistry and Nutrition, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan, Hubei 430062, PR China.
Food chemistry
|February 28, 2025
概括
一个新的移动应用程序使用大麻的图像进行深度学习,以快速,非破坏性地预测蛋白质和油含量. 这种方法为油作物传统分析提供了低成本的替代方案.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 分析黄油质量的传统方法 (蛋白质和油含量) 是低效的,需要大量的时间,劳动力和成本.
- 需要快速,非破坏性和具有成本效益的技术来评估农业部门的质量.
研究的目的:
- 开发一个移动应用程序,利用优化的深度学习模型实时,非破坏性预测大麻中的蛋白质和油含量.
- 评估不同深度学习模型和修剪技术的性能.
主要方法:
- 采集了大麻样品的图像采集.
- 开发和优化深度学习模型 (FasterNet-L) 用于基于图像的质量预测.
- 应用神经修剪技术 (通过生长规范化进行神经修剪) 来提高模型效率.
主要成果:
- FasterNet-L模型实现了高预测准确性,油的R值为0.9366,蛋白质含量为0.8828.
- 通过生长规范化的神经修剪显著提高了预测速度13.18%,并减少了模型大小15.79%.
- 开发的方法表现出强大的性能,预测误差低 (RMSEP) 和良好的预测能力 (RPD).
结论:
- 一个由深度学习驱动的移动应用程序提供了一个可行的,低成本和高效的解决方案,用于实时确定大麻中的蛋白质和油含量.
- 优化的深度学习方法,包括模型修剪,提高预测速度和减少模型大小,使其适用于现场应用.
- 这项技术有可能在各种油作物的快速质量评估中得到广泛应用.
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