Rapid detection and discrimination of plant leaves using laser-induced breakdown spectroscopy

Abstract

The wide diversity of species and the remarkable variation in morphological features that allow plants to adapt to a wide range of terrestrial environments is a fact that highlights the fundamental and crucial role of plants in the field of biodiversity studies. Currently, research on leaf classification is limited and in its early stages. A novel classification system based on laser-induced breakdown spectroscopy (LIBS) technology was proposed in this paper, integrated with machine learning for real-time, in situ detection and analysis of leaves. Four representative leaf samples—Ilex chinensis, Camellia japonica, Cinnamomum camphora, and Osmanthus fragrans—were subjected to spectral analysis and machine learning techniques. Spectral analysis revealed distinct spectral lines corresponding to elements such as Ca, Al, Mg, Na, and Fe, alongside common elements including C, N, and O. Principal component analysis (PCA) was employed to reduce the dimensionality of the spectral data, and the first 13 principal components used in this study captured 98.76% of the total variance. Following this, support vector machine (SVM), backpropagation artificial neural network and convolutional neural network (CNN) algorithms were applied for machine learning on the principal components to develop leaf recognition classification models. Through comparison, the CNN algorithm, boasting a classification accuracy of up to 94.44%, was ultimately selected. The models established by SVM and back propagation artificial neural network achieved accuracy of only 83.33% and 90.00%, respectively. The results suggest that integrating LIBS with machine learning is an effective and precise approach for leaf classification, offering promising applications in biodiversity research.