Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability
Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation cap...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/12/2939 |
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| author | Mohammad Rajabi-Sarkhani Yousef Abbaspour-Gilandeh Abdolmajid Moinfar Mohammad Tahmasebi Miriam Martínez-Arroyo Mario Hernández-Hernández José Luis Hernández-Hernández |
| author_facet | Mohammad Rajabi-Sarkhani Yousef Abbaspour-Gilandeh Abdolmajid Moinfar Mohammad Tahmasebi Miriam Martínez-Arroyo Mario Hernández-Hernández José Luis Hernández-Hernández |
| author_sort | Mohammad Rajabi-Sarkhani |
| collection | DOAJ |
| description | Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability. |
| first_indexed | 2024-03-08T21:04:33Z |
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| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-08T21:04:33Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj.art-81134b4049d344f9bc2618b18bd935d82023-12-22T13:46:23ZengMDPI AGAgronomy2073-43952023-11-011312293910.3390/agronomy13122939Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed ViabilityMohammad Rajabi-Sarkhani0Yousef Abbaspour-Gilandeh1Abdolmajid Moinfar2Mohammad Tahmasebi3Miriam Martínez-Arroyo4Mario Hernández-Hernández5José Luis Hernández-Hernández6Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranNational Technology of Mexico/Acapulco Institute of Technology, Acapulco 39905, MexicoFaculty of Engineering, Autonomous University of Guerrero, Chilpancingo 39070, MexicoNational Technological of Mexico/Chilpancingo Institute of Technology, Chilpancingo 39070, MexicoPeanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability.https://www.mdpi.com/2073-4395/13/12/2939seed viabilityspectrometryvariable selection methodmachine learningnondestructive diagnosismetaheuristic algorithm |
| spellingShingle | Mohammad Rajabi-Sarkhani Yousef Abbaspour-Gilandeh Abdolmajid Moinfar Mohammad Tahmasebi Miriam Martínez-Arroyo Mario Hernández-Hernández José Luis Hernández-Hernández Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability Agronomy seed viability spectrometry variable selection method machine learning nondestructive diagnosis metaheuristic algorithm |
| title | Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability |
| title_full | Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability |
| title_fullStr | Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability |
| title_full_unstemmed | Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability |
| title_short | Identifying Optimal Wavelengths from Visible–Near-Infrared Spectroscopy Using Metaheuristic Algorithms to Assess Peanut Seed Viability |
| title_sort | identifying optimal wavelengths from visible near infrared spectroscopy using metaheuristic algorithms to assess peanut seed viability |
| topic | seed viability spectrometry variable selection method machine learning nondestructive diagnosis metaheuristic algorithm |
| url | https://www.mdpi.com/2073-4395/13/12/2939 |
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