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Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning.
Chen, Mengyuan; Lin, Chenfeng; Sun, Yongqi; Yang, Rui; Lu, Xiangyu; Lou, Weidong; Deng, Xunfei; Zhao, Yunpeng; Liu, Fei.
Afiliación
  • Chen M; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
  • Lin C; Systematic & Evolutionary Botany and Biodiversity Group, MOE Key Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
  • Sun Y; Institute of Crop Science, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China.
  • Yang R; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
  • Lu X; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
  • Lou W; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
  • Deng X; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
  • Zhao Y; Systematic & Evolutionary Botany and Biodiversity Group, MOE Key Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
  • Liu F; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Plants (Basel) ; 13(11)2024 May 29.
Article en En | MEDLINE | ID: mdl-38891310
ABSTRACT
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Plants (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Plants (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza