基于1DQCNN和Vis/NIR光谱的果水核等级分类的便携式非破坏性仪器的设计
Haijian Wu1, Yong Lin2, Wenbin Zhang3
1School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China.
一个新的便携式设备使用可见/近红外光谱学和一维方位卷积神经网络 (1DQCNN) 来准确地实时检测果水核疾病. 这项技术可以在果园环境中快速,无损地对果进行分类.
科学领域:
- 农业工程 农业工程
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 果的水核病在生长和成熟期间对非破坏性鉴定构成挑战.
- 精确的,实时分类果水芯对于质量控制和收获管理至关重要.
研究的目的:
- 开发一种便携式设备,用于实时,非破坏性识别和果水芯的分级.
- 提高水心检测在果园环境中的准确性和实用性.
主要方法:
- 使用可见/近红外 (Vis/NIR) 光谱与一个便携式AI-OX2000-13微光谱仪.
- 为分类开发了一种一维的正方形卷积神经网络 (1DQCNN) 模型.
- 集成BiSeNet和RIFE算法来构建一个3D果水核模型进行量化.
主要成果:
- 1DQCNN模型在果水核等级方面实现了98.05%的分类准确度.
- 便携式仪器在现实世界果园应用中表现出高精度和实用性.
- 在水核分类中表现优于传统方法和常规卷积神经网络 (CNN) 模型.
结论:
- 开发的便携式Vis/NIR光谱仪设备与1DQCNN相结合,为实时果水芯检测提供了有效的解决方案.
- 该系统能够快速,非破坏性地分类果,满足果园环境的要求.
- 该技术可方便精确量化和分类水芯严重性的四个级别.
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