Chen Yuanfeng, Liu Li, Rao Yuan, Zhang Xiaodan, Zhang Wu, Jin Xiu
College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, China.
College of Horticulture, Anhui Agricultural University, Hefei 230001, China.
Foods. 2023 Mar 10;12(6):1178. doi: 10.3390/foods12061178.
The "Dangshan" pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of "Dangshan" pears. This paper employs the merging of NIRS features and image features for the detection of "Dangshan" pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the "Dangshan" pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.
“砀山”梨发绵反应是一种生理病害，给果农造成巨大损失且导致养分不足。这种病害的主要原因是梨中硼和钙短缺以及梨的水分流失。本文利用近红外光谱（NIRS）与计算机视觉技术（CVS）的融合来检测“砀山”梨的发绵反应病害。本文采用近红外光谱特征与图像特征的合并来检测“砀山”梨发绵反应病害。近红外光谱（NIRS）反映被测样品中各种生化结构中含氢基团和其他成分的有机物质信息，而计算机视觉技术（CVS）捕捉病害的图像信息。本研究比较了不同融合模型的结果。与其他策略相比，结合光谱特征和图像特征的融合模型具有更好的性能。这些融合模型比单特征模型具有更好的模型效果，并且这些模型的效果可能会因为融合建模选择的不同图像深度特征而有所不同。因此，进一步比较了使用不同图像深度特征进行融合建模的模型结果。结果表明，本研究中深度模型越深，提取得到的图像特征与光谱特征的融合建模效果越好。分别融合提取的近红外光谱特征和图像特征的多层感知器分类模型与深度可分离卷积神经网络的组合是最佳组合，该模型的准确率（0.972）、精确率（0.974）、召回率（0.972）和F1值（0.972）在其他模型中最高。本文表明，利用近红外光谱与基于图像的神经网络特征的融合可以显著提高“砀山”梨发绵反应病害的检测准确率。它还为光谱和图像的几种无损检测技术提供了理论依据。
