基于近红外透射光谱与卷积神经网络的蜜柚枯水病无损检测方法

Non-destructive Detection of Pomelo Granulation Disease Based on Near-infrared Transmission Spectroscopy and Convolutional Neural Networks

摘要:

枯水病是蜜柚采后的常见病害，枯水病出现时会影响蜜柚果实口感，甚至使其失去食用价值。作为一种大型厚皮水果，难以通过肉眼及果实外表判断蜜柚内部是否存在枯水病害。本研究基于近红外透射光谱检测方法结合一维卷积神经网络（One-dimensional convolutional neural networks, 1D-CNN）建立蜜柚枯水病快速无损检测模型。采用实验室自主研发的可见-近红外全透射光谱测量系统，以红肉蜜柚为研究对象，采用短积分模式采集样本的近红外透射光谱波长范围（562.03~1110.16nm）数据进行分析，在没有光谱数据预处理情况下采用Kennard-Stones算法将样本划分训练集和测试集，随后建立一维卷积神经网络蜜柚枯水判别模型，经测试，模型对于枯水程度分类准确率达到98.15%。与采用原始光谱数据与经多项式平滑处理（Savitzky-Golay, SG）和使用竞争性自适应重加权算法（Competitive adapative reweighted sampling, CARS）筛选特征变量后的反向传播神经网络（Backpropagation neural network, BPNN）、核极限学习机（Kernel extreme learning machine, K-ELM）、极限梯度提升树（Extreme gradient boosting, XGBoost）等传统判别模型的判别结果进行对比，最后使用数据增强方法对原始光谱数据进行扩充，以验证卷积神经网络模型稳定性。结果表明，本文提出的卷积神经网络模型在不使用复杂预处理以及特征筛选方法的情况下，对蜜柚内部枯水以及枯水程度的判别效果最佳，且能够在光谱处理过程选择有效信息，极大地提升了模型判别效率，对实现蜜柚内部枯水程度快速、准确地无损判别具有借鉴意义。

Abstract:

Granulation is a common post-harvest disease of citrus fruits, particularly in pomelos. When this disease occurs, it will affect the taste of pomelo fruits and even make them lose their edible value. Pomelos are large, thick-skinned fruits which are difficult to visually detect their internal granulation. The rapid, non-destructive detection of granulation of pomelos was explored by using near-infrared transmission spectroscopy combined with one-dimensional convolutional neural networks. A self-developed visible-near-infrared full-transmission spectroscopy measurement system was used to collect data from red-flashed pomelos. Data were collected in the 562.03~1110.16nm range under short integration mode. Without spectral preprocessing, the Kennard-Stones algorithm divided samples into training and testing sets. Subsequently, a one-dimensional convolutional neural network model for pomelo dryness identification was established. The network had a seven-layer structure. Testing showed that the model achieved a classification accuracy of 98.15% for granulation levels. Compared with traditional models such as backpropagation neural network (BPNN), kernel extreme learning machine (K-ELM), and extreme gradient boosting tree (XGBoost), which used raw spectral data and polynomial smoothing processing (SG) and competitive adaptive reweighted sampling (CARS) to extract feature variables. Finally, data augmentation further validated the model’s stability. The proposed convolutional neural network model effectively identified internal granulation without complex preprocessing. It can select effective information during the spectral processing, greatly improving the model identification efficiency. The researh provided a reference for the application of deep learning methods to achieve rapid and accurate non-destructive identification of the internal granulation level of pomelo.