{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:37:17Z","timestamp":1760369837326,"version":"3.30.1"},"reference-count":25,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.32604\/iasc.2023.029446","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T02:42:18Z","timestamp":1658198538000},"page":"1815-1829","source":"Crossref","is-referenced-by-count":2,"title":["Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples"],"prefix":"10.32604","volume":"35","author":[{"given":"Saud","family":"S. Alotaibi","sequence":"first","affiliation":[]}],"member":"17807","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.3390\/s19051090","article-title":"Recent applications of multispectral imaging in seed phenotyping and quality monitoring\u2014an overview","volume":"19","author":"ElMasry","year":"2019","journal-title":"Sensors"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.3390\/s19051190","article-title":"Classification method for viability screening of naturally aged watermelon seeds using FT-NIR spectroscopy","volume":"19","author":"Yasmin","year":"2019","journal-title":"Sensors"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"112162","DOI":"10.1016\/j.indcrop.2020.112162","article-title":"Quality classification of Jatropha curcas seeds using radiographic images and machine learning","volume":"146","author":"De Medeiros","year":"2020","journal-title":"Industrial Crops and Products"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"e66428","DOI":"10.1371\/journal.pone.0066428","article-title":"Yield trends are insufficient to double global crop production by 2050","volume":"8","author":"Ray","year":"2013","journal-title":"PLoS ONE"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1590\/0103-9016-2015-0007","article-title":"Seed vigor testing: An overview of the past, present and future perspective","volume":"72","author":"Marcos Filho","year":"2015","journal-title":"Scientia Agricola"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.biosystemseng.2018.09.015","article-title":"X-ray CT image analysis for morphology of muskmelon seed in relation to germination","volume":"175","author":"Ahmed","year":"2018","journal-title":"Biosystems Engineering"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.indcrop.2020.112162","article-title":"Quality classification of Jatropha curcas seeds using radiographic images and machine learning","volume":"146","author":"Medeiros","year":"2020","journal-title":"Industrial Crops and Products"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"e03477","DOI":"10.1016\/j.heliyon.2020.e03477","article-title":"Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA)","volume":"6","author":"Andrade","year":"2020","journal-title":"Heliyon"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1021\/ed039p546","article-title":"Spectrometric identification of organic compounds","volume":"13","author":"Silverstein","year":"1962","journal-title":"Journal of Chemical Education"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.foodchem.2016.11.064","article-title":"Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and non-linear calibration methods","volume":"221","author":"Li","year":"2017","journal-title":"Food Chemistry"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.infrared.2019.02.008","article-title":"Determination of viability of Retinispora (Hinoki cypress) seeds using FT-NIR spectroscopy","volume":"98","author":"Mukasa","year":"2019","journal-title":"Infrared Physics & Technology"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.postharvbio.2018.12.016","article-title":"Non-destructive porosity mapping of fruit and vegetables using X-ray CT","volume":"150","author":"Nugraha","year":"2019","journal-title":"Postharvest Biology and Technology"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2020.114212","volume":"365","author":"Benedet","year":"2020","journal-title":"Geoderma"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1063\/5.0024017","article-title":"Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods","volume":"30","author":"Bantan","year":"2020","journal-title":"Chaos: An Interdisciplinary Journal of Nonlinear Science"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-018-0272-0","article-title":"Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis","volume":"14","author":"Awty-Carroll","year":"2018","journal-title":"Plant Methods"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-020-00602-8","article-title":"DiSCount: Computer vision for automated quantification of Striga seed germination","volume":"16","author":"Masteling","year":"2020","journal-title":"Plant Methods"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-020-00591-8","article-title":"The BELT and phenoSEED platforms: Shape and colour phenotyping of seed samples","volume":"16","author":"Halcro","year":"2020","journal-title":"Plant Methods"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-020-00653-x","article-title":"Developmental normalization of phenomics data generated by high throughput plant phenotyping systems","volume":"16","author":"Lozano-Claros","year":"2020","journal-title":"Plant Methods"},{"key":"ref19","first-page":"1","article-title":"Vigor-S: System for automated analysis of soybean seed vigor","volume":"42","author":"Mayara","year":"2020","journal-title":"Journal of Seed Science"},{"key":"ref20","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"12322","article-title":"Satellite image time series classification with pixel-set encoders and temporal self-attention","year":"2020"},{"key":"ref21","series-title":"1st Int. Conf. On Multimedia Analysis And Pattern Recognition (MAPR)","first-page":"1","article-title":"A vision-based method for automatic evaluation of germination rate of rice seeds","author":"Nguyen","year":"2018"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-020-00699-x","article-title":"Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops","volume":"16","author":"Genze","year":"2020","journal-title":"Plant Methods"},{"key":"ref23","unstructured":"N. Sandeep, P. Pani, R. Nair and G. Varma, \u201cAutomated seed quality testing system using GAN & active learning1\u20139, 2021. [Online]. Available: https:\/\/arxiv.org\/abs\/2110.00777."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/S0022-1694(01)00594-7","article-title":"Power of the mann-kendall and spearman\u2019s rho tests for detecting monotonic trends in hydrological series","volume":"259","author":"Yue","year":"2002","journal-title":"Journal of hydrology"},{"key":"ref25","unstructured":"Ken Pletcher, \u201cPaddy Seed Data [Data set]. Kaggle,\u201d 2020. [Online]. Available: https:\/\/www.kaggle.com\/dataset\/c6caf46ef419a4cb653b4f8872a8136bb17151b106abd84821742b19d6db2447."}],"container-title":["Intelligent Automation & Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/ueditor\/files\/iasc\/TSP_IASC-35-2\/TSP_IASC_29446\/TSP_IASC_29446.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T21:30:09Z","timestamp":1733520609000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/iasc\/v35n2\/48922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.32604\/iasc.2023.029446","relation":{},"ISSN":["1079-8587"],"issn-type":[{"type":"print","value":"1079-8587"}],"subject":[],"published":{"date-parts":[[2023]]}}}