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- Title
NONDESTRUCTIVE PREDICTION OF RICE SEED VIABILITY USING SPECTRAL AND SPATIAL INFORMATION MODELING OF VISIBLE-NEAR INFRARED HYPERSPECTRAL IMAGES.
- Authors
Suk-Ju Hong; Tao Yang; Sang-Yeon Kim; EungChan Kim; ChangHyup Lee; Nurhisna, Nandita Irasaulul; Sungjay Kim; Seung-Woo Roh; Jiwon Ryu; Ghiseok Kim
- Abstract
Rice is one of the world's most important food crops, and rice seed viability is an important factor in rice crop production. In this study, a visible-near infrared (vis-NIR) hyperspectral imaging system and spectral-spatial information modeling are used to predict the viability of rice seeds. Experimental samples are prepared using seeds harvested in two different years and artificially aged for various periods. Vis-NIR hyperspectral acquisition and germination tests of the prepared seed samples are performed. Partial least square (PLS)-discriminant analysis, a support vector machine (SVM), a PLS-SVM, a PLS-artificial neural network, and a one-dimensional-convolutional neural network (CNN) for the mean spectra of seeds, as well as a CNN, a PLS-CNN, and dual branch networks for the hyperspectral images of the seeds are applied for viability prediction modeling. Result shows that an accuracy of approximately 90% and high f1 scores can be obtained in most models. Furthermore, it is confirmed that models using spectral and spatial information can classify hard samples more effectively.
- Subjects
SEED viability; INFORMATION modeling; HYPERSPECTRAL imaging systems; INFRARED imaging; FOOD crops; RICE seeds
- Publication
Journal of the ASABE, 2023, Vol 66, Issue 5, p997
- ISSN
2769-3295
- Publication type
Article
- DOI
10.13031/ja.14982