Applications of machine learning in pine nuts classification

Abstract

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.

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review

“…Arndt et al [ 52 ] and Arndt et al [ 42 ] developed multiclass models to determine the geographical origin of almonds using SVM, which is a method that can be used to train linear and non-linear classifiers, for classification [ 77 ]. Huang et al [ 59 ] also used machine learning methods, such as the RF, NB, or the multilayer perceptron (MLP), to identify the cultivars of pine nuts. Menevseoglu et al [ 36 ] successfully identified ground almonds that had been adulterated with apricot kernels by applying different machine learning algorithms.…”

Non-Destructive Classification of Maize Seeds Based on RGB and Hyperspectral Data with Improved Grey Wolf Optimization Algorithms

“…Consequently, some scholars have started to explore the fusion of information from multiple types of features to perform classification discrimination tasks. Huang [27] Hyperspectral ELM 5 types of wheat 86.26% Bao [28] RGB MLP, LDA, SVM etc. 5 types of maize Each model achieved 93% Xu [29] Deep learning RGB MF Swin-Transformer 19 types of maize 96.47% Bi [30] Hyperspectral image CNN-LSTM 5 types of maize 95.26% Wang [31] NIRS CNN, RNN, LSTM, etc.…”

The VIS-Spectrometric Data of Scots Pine Individual Seed Reveal Forecasting Potential for Container-Grown Germination and Seedling’s Dickson Quality Index

“…The accuracy of non-destructive [11][12][13] detection of forest seeds using electromagnetic waves from different regions increases with the assessment of each single seed [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] individually, determined by the focal distance [33] from the sensor to the place of reflection of the electromagnetic beam from the top of the seed coat [34]. At the same time, depending on the type and nature of exposure to electromagnetic radiation, the biophysical [13,35] (spectrometric) parameters of the seed correlate with different properties and indicators of a single seed [2,5,7,9,11,12,[12][13][14][15][16]21,[23][24][25][26][27][28]31,34,…”

“…Esteve Agelet et al (2014) conclude from a systematic search of 168 references for the identification of a single seed that "although no measurement mode (reflectance, transmittance) have lead to the best re-ported calibrations, when dealing with heterogenic seeds reflectance is the best working mode [123]". Most studies of the forest single seed [14][15][16]20,21,24,[26][27][28][29][30][31][32]99,123,136]. Expensive devices are used to create and detect electromagnetic radiation with long exposure.…”