研究不完善的小麦谷物识别方法,利用高光谱成像技术
Hongtao Zhang1, Li Zheng1, Lian Tan1
1College of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种超光谱成像方法,以快速识别不完美的小麦粒,这对粮食安全至关重要. 移动网V2模型实现了最高的准确性,展示了技术.
科学领域:
- 农业科学 农业科学
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 小麦是中国的主要粮食作物,对农业和粮食安全至关重要.
- 不完美的小麦粒显著降低了整体质量,并影响了粮食安全.
- 目前对不完美的谷物的检测方法往往是破坏性的或低效的.
研究的目的:
- 开发一种快速,非破坏性的方法,使用高光谱成像识别完美和不完美的小麦粒.
- 为了比较不同机器学习模型对小麦谷物分类的性能.
- 加强小麦质量评估,为粮食安全做出贡献.
主要方法:
- 收集了来自7种类型的2100粒小麦的可见近红外高光谱数据.
- 应用Savitzky-Golay和连续投影算法用于数据预处理和维度减少,选择33个有效的光谱数据点.
- 利用主要组件分析来识别最佳波长 (647.57nm,591.78nm,568.36nm) 和优化的支持矢量机,卷积神经网络和使用粒子群优化的MobileNet V2模型.
主要成果:
- 优化的支持向量机,卷积神经网络和MobileNet V2模型的全面识别率分别为93.71%,95.14%和97.71%.
- 与其他测试模型相比,MobileNet V2模型显示出更高的识别效率.
- 超光谱成像与分类模型相结合,准确识别了不完美的小麦粒.
结论:
- 超光谱成像为识别不完美的小麦粒提供了一种有希望的非破坏性方法.
- 移动网V2模型为此分类任务提供了高精度和高效率.
- 这项技术可以为改善小麦质量控制和确保粮食安全做出重大贡献.
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