Variety Classification of Coated Maize Seeds Based on Raman Hyperspectral Imaging

2021 ◽  
pp. 120772
Author(s):  
Qingyun Liu ◽  
Zuchao Wang ◽  
Yuan Long ◽  
Chi Zhang ◽  
Shuxiang Fan ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Maize Seeds ◽  
Variety Classification

scholarly journals Application of hyperspectral imaging and chemometrics for variety classification of maize seeds

RSC Advances ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 1337-1345 ◽  
Author(s):  
Yiying Zhao ◽  
Susu Zhu ◽  
Chu Zhang ◽  
Xuping Feng ◽  
Lei Feng ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Crop Yield ◽  
Maize Seeds ◽  
Variety Classification

Hyperspectral imaging provides an effective way for seed variety classification for assessing variety purity and increasing crop yield.

scholarly journals Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4391 ◽  
Author(s):  
Aimin Miao ◽  
Jiajun Zhuang ◽  
Yu Tang ◽  
Yong He ◽  
Xuan Chu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Procrustes Analysis ◽  
Waxy Maize ◽  
Maize Seeds ◽  
Variety Classification ◽  
Low Dimensional

Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.

<i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>

2017 ◽  
Author(s):  
Xin Zhao ◽  
Wei Wang ◽  
Xuan Chu ◽  
Hongzhe Jiang ◽  
Beibei Jia ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Classification ◽  
Maize Kernels

Classification of maize seeds of different years based on hyperspectral imaging and model updating

2016 ◽  
Vol 122 ◽  
pp. 139-145 ◽  
Author(s):  
Min Huang ◽  
Jinya Tang ◽  
Bao Yang ◽  
Qibing Zhu
Keyword(s):  
Hyperspectral Imaging ◽  
Model Updating ◽  
Maize Seeds

Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging

2012 ◽  
Vol 109 (3) ◽  
pp. 482-489 ◽  
Author(s):  
Izumi Sone ◽  
Ragnar L. Olsen ◽  
Agnar H. Sivertsen ◽  
Guro Eilertsen ◽  
Karsten Heia
Keyword(s):  
Atlantic Salmon ◽  
Hyperspectral Imaging ◽  
Salmo Salar ◽  
Atlantic Salmon Salmo Salar

Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Christan Hail Mendigoria ◽  
Ronnie Concepcion ◽  
Elmer Dadios ◽  
Heinrick Aquino ◽  
Oliver John Alaias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phaseolus Vulgaris ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dry Beans ◽  
Variety Classification
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