PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy
Version 1
: Received: 24 November 2023 / Approved: 24 November 2023 / Online: 27 November 2023 (05:17:44 CET)
How to cite:
Rajabi-Sarkhani, M.; Gilandeh, Y.A.; Moinfar, A.; Tahmasebi, M.; Cruz-Gámez, E.D.L.; Hernández-Hernández, M.; Hernández-Hernández, J.L. Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy. Preprints2023, 2023111595. https://doi.org/10.20944/preprints202311.1595.v1
Rajabi-Sarkhani, M.; Gilandeh, Y.A.; Moinfar, A.; Tahmasebi, M.; Cruz-Gámez, E.D.L.; Hernández-Hernández, M.; Hernández-Hernández, J.L. Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy. Preprints 2023, 2023111595. https://doi.org/10.20944/preprints202311.1595.v1
Rajabi-Sarkhani, M.; Gilandeh, Y.A.; Moinfar, A.; Tahmasebi, M.; Cruz-Gámez, E.D.L.; Hernández-Hernández, M.; Hernández-Hernández, J.L. Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy. Preprints2023, 2023111595. https://doi.org/10.20944/preprints202311.1595.v1
APA Style
Rajabi-Sarkhani, M., Gilandeh, Y.A., Moinfar, A., Tahmasebi, M., Cruz-Gámez, E.D.L., Hernández-Hernández, M., & Hernández-Hernández, J.L. (2023). Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy. Preprints. https://doi.org/10.20944/preprints202311.1595.v1
Chicago/Turabian Style
Rajabi-Sarkhani, M., Mario Hernández-Hernández and José Luis Hernández-Hernández. 2023 "Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy" Preprints. https://doi.org/10.20944/preprints202311.1595.v1
Abstract
Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.
Keywords
Convolutional Neural Network; Seed viability; Principal component analysis; Outlier; Support vector machine; Linear Discriminant Analysis
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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