AUTHOR=Liu Zhuo , Al-Sarayreh Mahmoud , Li Yanjie , Yuan Zhilin 

TITLE=Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks

JOURNAL=Frontiers in Forests and Global Change

VOLUME=Volume 6 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2023.1179910

DOI=10.3389/ffgc.2023.1179910

ISSN=2624-893X

ABSTRACT=Hyperspectral imagery and machine learning have proven to be powerful, non-invasive, and chemical-free tools for studying tree symbiotic fungifungi. However, traditional machine learning requires manual feature extraction (feature engineering) of spectral and spatial features of tree symbiotic fungifungi. Deep convolutional neural networks (CNNs) can extract self and robust features directly from the raw data. In the current study, a deep CNN architecture is proposed to recognize the speciesisolates of dark septate endophytic (DSE) fungal fungi in hyperspectral images. The performance of different CNN approaches (two-dimensional and three-dimensional CNNs) was compared and evaluated based on two independent datasets collected using visible-near-infrared (VNIR) and short-wave-infrared (SWIR) hyperspectral imaging systems. Moreover, the impact of different spectral pre-processing techniques was investigated. The results show that a hybrid CNN architecture (3D-2D CNN), which combines three and two-dimensional CNNs, achieved the best performance for the classification of fungal isolates fungi on SWIR hyperspectral data compared to the same architecture on VNIR hyperspectral data. The best performance is 100% for precision, recall, and overall accuracy. The results also demonstrate that combining different pre-processing techniques on raw SWIR spectra can significantly improve the performance of the CNN models for fungifungal classification. The hybrid CNN approach with SWIR hyperspectral data provides an efficient method for classifying fungifungal speciesisolates, which can contribute to the development of accurate and non-destructive tools for evaluating the occurrence of fungifungal speciesisolates on trees. Such tools can be beneficial for both sustainable agriculture and preserving fungal diversity.