Applications of machine learning in pine nuts classification.
Applications of machine learning in pine nuts classification.
复制标题
机器学习在松子分类中的应用。
DOI：
10.1038/s41598-022-12754-9
复制
发表时间：
2022-05-25
影响因子：
4.6
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Huang B;Liu J;Jiao J;Lu J;Lv D;Mao J;Zhao Y;Zhang Y
松子不仅是松树繁殖和造林的重要媒介，而且是人们经常食用的营养价值很高的坚果。然而，由于物种之间的形态相似性，很难区分松仁。因此，提高松子的质量，快速、无损地解决掺假问题具有重要意义。本研究以白皮松、云南松、黑松、华山松、马尾松、湿地松和黄山松7种松仁为研究对象。采集了7种松仁的近红外光谱数据210个，采用决策树、随机森林、多层感知器、支持向量机和朴素贝叶斯5种机器学习方法对松仁的种类进行识别。使用303张图像收集形态学数据，以构建基于五种卷积神经网络（CNN）模型（VGG 16，VGG 19，Xception，InceptionV 3和ResNet 50）的分类模型。近红外光谱实验结果表明，最佳分类模型为MLP，分类精度接近0.99。另一个图像的实验结果表明，最好的分类模型是InceptionV 3，准确率接近0.964。结果表明，951-957 nm、1，147 ~ 1，154 nm、1，907 ~ 1，927 nm、2，227 ~ 2，254 nm四个波段与松子的分类密切相关。本研究表明，机器学习对松子的分类是有效的，为不同种类松子的快速、无损、准确分类提供了解决方案和科学方法。
Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.
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影响因子：
3.4
作者：
Gernandt, DS;López, GG;Liston, A
通讯作者：
Liston, A
DOI：
10.1073/pnas.2022302118
发表时间：
2021-05-18
影响因子：
11.1
作者：
Jin WT;Gernandt DS;Wehenkel C;Xia XM;Wei XX;Wang XQ
通讯作者：
Wang XQ
影响因子：
1.2
作者：
Herold, N.;You, Y.;Seton, M.
通讯作者：
Seton, M.
影响因子：
3.3
作者：
Pandit, Ramesh;Travadi, Tasnim;Joshi, Madhvi
通讯作者：
Joshi, Madhvi
影响因子：
4.2
作者：
Golonka, J;Bocharova, NY;Wildharber, J
通讯作者：
Wildharber, J