This study aimed to identify the cultivated and wild growth modes of mushrooms using Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning algorithms. A total of 230 mushroom slice samples were collected, and their LIBS spectral data were analyzed. Multiple machine learning algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were employed for classification. The results showed that the integration of Principal Component Analysis (PCA) significantly reduced the modeling analysis time for MLP, SVM, and KNN. Among the six classification methods, PCA+KNN achieved the highest average accuracy of 94.7%. This indicates the feasibility of using LIBS combined with machine learning algorithms for distinguishing different types of mushroom samples. The innovation of this study lies in the utilization of PCA for dimensionality reduction and the combination of various machine learning algorithms for mushroom sample classification, leading to improved accuracy and modeling speed.

Jiayuan Tang, Shaohua Tang. Identification of Cultivated and Wild Mushrooms Using Laser-Induced Breakdown Spectroscopy Technology Combined with Machine Learning Algorithms. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 161-168. https://doi.org/10.25236/AJCIS.2023.061323.

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