Tags

Type your tag names separated by a space and hit enter

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.
Sensors (Basel). 2018 Mar 08; 18(3)S

Abstract

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

Authors+Show Affiliations

Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China. ztt_cau@163.com.National R&D Center for Agro-Processing Equipments, College of Engineering, China Agricultural University, Beijing 100083, China. weiwensong8@163.com.Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China. binzhaodave@outlook.com.Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China. 18763825710@163.com.Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China. 13limingliu@cau.edu.cn.College of Science, China Agricultural University, Beijing 100083, China. cauyanglm@163.com.Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China. wangjh63@163.com.Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China. sqcau@126.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29517991

Citation

Zhang, Tingting, et al. "A Reliable Methodology for Determining Seed Viability By Using Hyperspectral Data From Two Sides of Wheat Seeds." Sensors (Basel, Switzerland), vol. 18, no. 3, 2018.
Zhang T, Wei W, Zhao B, et al. A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. Sensors (Basel). 2018;18(3).
Zhang, T., Wei, W., Zhao, B., Wang, R., Li, M., Yang, L., Wang, J., & Sun, Q. (2018). A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. Sensors (Basel, Switzerland), 18(3). https://doi.org/10.3390/s18030813
Zhang T, et al. A Reliable Methodology for Determining Seed Viability By Using Hyperspectral Data From Two Sides of Wheat Seeds. Sensors (Basel). 2018 Mar 8;18(3) PubMed PMID: 29517991.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. AU - Zhang,Tingting, AU - Wei,Wensong, AU - Zhao,Bin, AU - Wang,Ranran, AU - Li,Mingliu, AU - Yang,Liming, AU - Wang,Jianhua, AU - Sun,Qun, Y1 - 2018/03/08/ PY - 2018/01/15/received PY - 2018/03/05/revised PY - 2018/03/06/accepted PY - 2018/3/9/entrez PY - 2018/3/9/pubmed PY - 2018/6/13/medline KW - PLS-DA KW - SVM KW - dataset KW - hyperspectral imaging KW - seed viability JF - Sensors (Basel, Switzerland) JO - Sensors (Basel) VL - 18 IS - 3 N2 - This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner. SN - 1424-8220 UR - https://www.unboundmedicine.com/medline/citation/29517991/A_Reliable_Methodology_for_Determining_Seed_Viability_by_Using_Hyperspectral_Data_from_Two_Sides_of_Wheat_Seeds_ L2 - https://www.mdpi.com/resolver?pii=s18030813 DB - PRIME DP - Unbound Medicine ER -