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基于高光谱成像的蓝莓微腐烂检测研究

Study of blueberry micro-rot detection based on hyperspectral imaging

  • 摘要: 为了探究蓝莓早期腐烂后时间及温度变化对其造成的影响,采用高光谱成像技术结合偏最小二乘法和反向传播神经网络算法,进行了理论分析和实验验证,取得了偏最小二乘法和反向传播神经网络对蓝莓腐烂的时间模型和温度模型,并比较了这两种算法的建模效果。结果表明,随着时间的增加,蓝莓腐烂的情况会进一步恶化;伴随着温度的提升,蓝莓腐烂强度逐步提高;基于偏最小二乘法建立的模型效果更适合腐烂蓝莓的检测,腐烂蓝莓的协方差系数为0.131和0.149,相关系数为0.932和0.921,误差较小且相关性趋于一致。偏最小二乘法建立的模型可以较好地显示时间及温度对腐烂蓝莓的影响,为蓝莓表面微腐烂检测提供了一定的参考。

     

    Abstract: In order to investigate the effects of time and temperature changes on blueberries after early decay, hyperspectral imaging technology combined with partial least squares and back-propagation neural network algorithms were used to carry out theoretical analysis and experimental validation, and partial least squares and back-propagation neural networks were used to obtain the time model and the temperature model of blueberry decay, and the modeling effects of these two algorithms were compared. The results show that with the increase of time, the blueberry decay will further deteriorate; along with the increase of temperature, the intensity of blueberry decay gradually increases, the effect of the model established based on the partial least squares method is more suitable for the detection of decayed blueberries, the coefficient of covariance and correlation coefficient of decayed blueberries are 0.131, 0.149, 0.932 and 0.921, respectively, and the error shows that the error is small and correlation tends to be consistent. The model established by partial least squares method can better show the effect of time and temperature on decayed blueberries, which provides a certain reference for the detection of micro-decay on the surface of blueberries.

     

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