Non-Destructive Viability Discrimination for Individual Scutellaria baicalensis Seeds Based on High-Throughput Phenotyping and Machine Learning
Keling Tu,
Ying Cheng,
Cuiling Ning,
Chengmin Yang,
Xuehui Dong,
Hailu Cao () and
Qun Sun ()
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Keling Tu: College of Agronomy and Biotechnology/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research of Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China
Ying Cheng: College of Agronomy and Biotechnology/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research of Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China
Cuiling Ning: Chengde Hengde Materia Medica Agricultural Technology Co., Ltd., Chengde 067000, China
Chengmin Yang: The Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing 100193, China
Xuehui Dong: College of Agronomy and Biotechnology/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research of Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China
Hailu Cao: Hengde Materia Medica (Beijing) Agricultural Technology Co., Ltd., Beijing 100070, China
Qun Sun: College of Agronomy and Biotechnology/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research of Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China
Agriculture, 2022, vol. 12, issue 10, 1-13
Abstract:
It is crucial to identify and select high-quality seeds for improving Scutellaria baicalensis yield. In this study, we present a non-destructive and accurate method for predicting Scutellaria baicalensis seed viability that used seed phenotypic data with machine-learning algorithms to distinguish between vital and dead seeds. Meanwhile, the SMOTE was used to balance the dataset and make the established viability discrimination model more efficient by avoiding problems of overfitting or under-fitting. The results showed that hyperspectral imaging (HSI) combined with detrend (DT) preprocessing and a support vector machine (SVM) model could predict Scutellaria baicalensis seed viability with a 93.3% accuracy, and increased the germination percentage of the seed lot to 99.1%, while machine vision imaging provided the highest 87.9% accuracy and 87.0% germination percentage. This strategy is suitable for large-scale Scutellaria baicalensis seed viability discrimination operations for ensuring seed quality, expanding the cultivation and production scales of Scutellaria baicalensis , and accelerating the present solving of the problem of short supply. It can help to accelerate the breeding of quality Scutellaria baicalensis varieties.
Keywords: non-destructive testing; germination prediction; hyperspectral image; machine vision; SVM (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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