Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

文献类型： 外文期刊

第一作者： Chen, Mengyuan

作者： Chen, Mengyuan;Yang, Rui;Lu, Xiangyu;Liu, Fei;Lin, Chenfeng;Zhao, Yunpeng;Sun, Yongqi;Lou, Weidong;Deng, Xunfei

作者机构：

关键词： Ginkgo biloba; sex identification; leaf morphology; hyperspectral imaging; machine learning

期刊名称：PLANTS-BASEL （ 影响因子：4.0； 五年影响因子：4.4 ）

ISSN： 2223-7747

年卷期： 2024 年 13 卷 11 期

页码：

收录情况： SCI

摘要： Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling-background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants.

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