基于深度学习的高光谱成像柑橘果实颗粒化检测研究

摘要：柑橘果实颗粒化是柑橘果实在成熟后期和采后贮藏过程中的主要生理病害，严重影响果实的品质。目前，柑橘颗 粒化快速、无损检测方法尚不成熟。本研究提出一种基于深度学习的无损颗粒化检测方法。以高光谱成像获得的透射光 谱预处理结果为输入，建立不同的模型。与最小二乘支持向量机（（ LS-SVM）和反向传播神经网络（（ BP-NN）相比，传统 卷积神经网络（CNN）的训练准确率最高，为88.02%。在CNN中加入批量归一化层后，实验结果表明，检测模型在训 练集和验证集的准确率分别为100%和97.9%。然后，通过对训练好的模型进行逐层分析，发现660.2–721.1 nm、708.5– 750 nm、806.5–847 nm 波段是与粒化关系密切的光谱。利用这些特征波段重建的模型在训练集和验证集中分别获得了 90.1% 和 85.4% 的准确率。这样，有效的波长选择可以找到与粒化高度相关的波段。结合一些功能组的研究，可以推断 出粒化过程中内部物质的变化，这可能为探究粒化的原因提供一些线索。开发柑橘粒化检测设备也具有重要意义

关键词：卷积神经网络，光谱分析，批量归一化，粒化，柚子，.内部质量

期刊：Food Analytical Methods

DOI：https://DOI.org/10.1007/s12161-020-01873-6

设备：GaiaSorter

Research on Citrus grandis Granulation Determination Based on Hyperspectral Imaging through Deep Learning

Abstract：Citrus fruit granulation is a major physical disorder during late maturity and post-harvest storage, and it greatly undermines the quality of fruit. Currently, there is still a lack of rapid and nondestructive methods to detect citrus granulation. This study proposes a nondestructive granulation detecting method based on deep learning. Different models were established with the input of preprocessed transmission spectra obtained by hyperspectral imaging. Conventional convolution neural network (CNN) got the best accuracy at 88.02% for training, compared with the least-square support vector machine (LS-SVM) and back- propagation neural network (BP-NN). After adding the batch-normalization layer to the CNN, the experimental results show that the detection model obtained a 100% accuracy in train set and 97.9% in validation set, respectively. And then, through analyzing the well-trained model layer by layer, bands of 660.2–721.1 nm, 708.5–750 nm and 806.5–847 nm were the spectra greatly related to granulation. The model rebuilt with these feature bands obtained 90.1% and 85.4% accuracy in train set and validation set, respectively. This way, effective wavelength selection can find bands highly correlated with granulation.Combined with some research on functional group, it is possible that inference to internal matter changes in granulation process, which may provide some hints to explore the reason of granulation. It is also meaningful to develop granulation-detecting equipment for citrus fruits

Keywords：Convolutionneuralnetwork .Spectrumanalyze .Batchnormalization .Granulation .Citrusgrandis .Internalquality