Utilization of Machine Learning and Hyperspectral Imaging Technologies for Classifying Coated Maize Seed Vigor: A Case Study on the Assessment of Seed DNA Repair Capability
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation and Hyperspectral Image Acquisition
2.2. Ground Truth Annotation: Seed Vigor Classifications
2.3. Data Preprocessing
2.4. ML Model Development
3. Results
3.1. Seed Vigor Classification
3.2. Oversampling Algorithm Performances
3.3. ML Model Performances
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ADASYN | Adaptive synthetic algorithm |
AI | Artificial intelligence |
ANOVA | Analysis of variance |
AUC | Area under the curve of the cumulative radicle emergence fitted curve |
BorderlineSMOTE | Borderline synthetic minority oversampling algorithm |
BP | Between-paper technique |
CARS | Competitive adaptive reweighted sampling |
CNNs | Convolutional neural networks |
DL | Deep learning |
DNA | Deoxyribonucleic acid |
HIS | Hyperspectral imaging |
LDA | Linear discriminant analysis |
MCC | Matthews correlation coefficient |
MGT | Mean radicle emergence times |
ML | Machine learning |
MSC | Multiplicative scatter correction |
PCA | Principal component analysis |
PLS-DA | Partial least squares discriminant analysis |
RE | Radicle emergence |
REMax | Maximum radicle emergence |
ROC | Receiver operating characteristic |
ROI | Region of interest |
SG | Savitzky–Golay |
SNV | Standard normal variate |
SVM | Support vector machine |
T50 | Radicle emergence speed |
U7525 | Uniformity of radicle emergence |
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Class Label | Number of Data in the Training Set | Number of Data in the Validation Set | Number of Data in the Test Set |
---|---|---|---|
(75%) | (15%) | (10%) | |
High-vigor seed lot | 6858 | 1372 | 914 |
Low-vigor seed lot | 1323 | 265 | 176 |
Total | 8181 | 1637 | 1090 |
Pretreatment | OG | SNV | MSC | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wavelength Selection | None | CARS | None | CARS | None | CARS | |||||||
Oversampling Technique | None | None | ADASYN | Borderline SMOTE | None | None | ADASYN | Borderline SMOTE | None | None | ADASYN | Borderline SMOTE | |
ELDA | Accuracy | 95.05 | 97.07 | 98.08 | 98.49 | 95.14 | 97.48 | 97.02 | 94.91 | 97.80 | 97.75 | ||
Sensitivity | 95.50 | 96.95 | 99.72 | 99.89 | 94.58 | 98.73 | 98.56 | 94.66 | 98.63 | 98.84 | |||
Specificity | 95.39 | 97.77 | 90.66 | 92.11 | 99.24 | 91.42 | 89.66 | 96.68 | 93.65 | 92.45 | |||
Precision | 99.28 | 99.61 | 97.96 | 98.29 | 99.89 | 98.24 | 97.85 | 99.50 | 98.73 | 98.46 | |||
F1-score | 97.10 | 98.26 | 98.83 | 99.09 | 97.16 | 98.48 | 98.21 | 97.02 | 98.68 | 98.65 | |||
MCC | 0.8133 | 0.8918 | 0.9343 | 0.9483 | 0.8180 | 0.9103 | 0.8945 | 0.8080 | 0.9207 | 0.9199 | |||
SVM | Accuracy | 97.25 | 94.73 | 99.13 | 99.08 | 97.66 | 99.45 | 99.45 | 97.02 | 99.31 | 99.27 | ||
Sensitivity | 96.91 | 94.05 | 99.40 | 99.34 | 97.27 | 100 | 99.89 | 96.55 | 99.89 | 99.89 | |||
Specificity | 99.35 | 100 | 97.78 | 97.78 | 100 | 96.91 | 97.31 | 100 | 96.53 | 96.28 | |||
Precision | 99.89 | 100 | 99.56 | 99.56 | 100 | 99.34 | 99.45 | 100 | 99.28 | 99.23 | |||
F1-score | 98.37 | 96.93 | 99.48 | 99.45 | 98.62 | 99.67 | 99.67 | 98.24 | 99.59 | 99.56 | |||
MCC | 0.8990 | 0.8021 | 0.9686 | 0.9669 | 0.9146 | 0.9807 | 0.9805 | 0.8905 | 0.9757 | 0.9742 |
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Wonggasem, K.; Wongchaisuwat, P.; Chakranon, P.; Onwimol, D. Utilization of Machine Learning and Hyperspectral Imaging Technologies for Classifying Coated Maize Seed Vigor: A Case Study on the Assessment of Seed DNA Repair Capability. Agronomy 2024, 14, 1991. https://doi.org/10.3390/agronomy14091991
Wonggasem K, Wongchaisuwat P, Chakranon P, Onwimol D. Utilization of Machine Learning and Hyperspectral Imaging Technologies for Classifying Coated Maize Seed Vigor: A Case Study on the Assessment of Seed DNA Repair Capability. Agronomy. 2024; 14(9):1991. https://doi.org/10.3390/agronomy14091991
Chicago/Turabian StyleWonggasem, Kris, Papis Wongchaisuwat, Pongsan Chakranon, and Damrongvudhi Onwimol. 2024. "Utilization of Machine Learning and Hyperspectral Imaging Technologies for Classifying Coated Maize Seed Vigor: A Case Study on the Assessment of Seed DNA Repair Capability" Agronomy 14, no. 9: 1991. https://doi.org/10.3390/agronomy14091991