深度学习和非破坏性检测技术的概述,用于对番茄的质量评估
Yuping Huang1, Ziang Li1, Zhouchen Bian1
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Foods (Basel, Switzerland)
|January 25, 2025
概括
深度学习增强了使用机械,电磁和电化学传感器进行非破坏性番茄质量评估. 本综述探讨了可靠数据分析的方法,提高了番茄行业的检测性能.
科学领域:
- 农业工程 农业工程
- 食品科学 食品科学 食品科学
- 计算机科学 (人工智能)
背景情况:
- 番茄因其营养价值和风味而在全球范围内种植.
- 非破坏性技术提供了高效的,非侵入性的番茄质量评估.
- 在非破坏性技术中处理大型数据集以获得强大的模型仍然具有挑战性.
研究的目的:
- 系统地概述番茄质量评估的非破坏性检测技术.
- 探索深度学习应用程序,以提高数据分析和检测性能.
- 为了比较基于传感器的机械,电磁和电化学方法.
主要方法:
- 审查机械,电磁和电化学传感器的原理和应用.
- 使用各种非破坏性技术进行番茄质量评估的分析.
- 在番茄质量评估中探索深度学习方法进行数据分析.
主要成果:
- 深度学习显著提高了非破坏性技术的稳定性和检测性能.
- 不同非破坏性方法的比较凸显了它们在番茄评估中的独特特征.
- 深度学习与光学设备的整合显示出有希望的结果.
结论:
- 深度学习对于在非破坏性番茄质量评估中克服数据处理挑战至关重要.
- 光学设备和深度学习的进步为番茄行业提供了有希望的未来.
- 与深度学习相结合的非破坏性技术对于农业工程应用至关重要.
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