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Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis

Cilt: 1 Sayı: 2 26 Aralık 2025
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Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis

Abstract

Field pea (Pisum sativum L.) is a significant legume crop in Pakistan, and the development of high-yielding varieties is essential for enhancing productivity. This study aimed to evaluate the morphological diversity of 25 pea genotypes using both image-based phenotyping and field-based traits. A total of 21 traits were analyzed. Principal Component Analysis (PCA) revealed a strong positive correlation among pod area, pod perimeter, pod length, pod weight, pod aspect ratio, number of seeds per pod, seed weight per pod, and number of nodes, with pod area showing the highest eigenvalue of 9.715. Cluster analysis identified four major clusters and six sub-clusters, highlighting significant genetic diversity in the germplasm. Correlation analysis revealed a highly significant positive correlation between pod weight and pod factor from density (r = 0.91), seed weight per pod (r = 0.88), and number of seeds per pod (r = 0.85). Path coefficient analysis indicated that pod area had the highest direct positive effect on pod weight (0.010), while pod length exhibited a negative direct effect (-0.002). Genetic variance estimates ranged from 41,108,350 for pod area to 0.00 for pod roundness, with phenotypic variance ranging from 50,332,040 for pod area to 0.00 for pod roundness. Broad-sense heritability was high for traits such as pod weight (0.80), pod area (0.82), seed weight per pod (0.81), and number of pods per plant (0.81). These findings highlight the strong genetic influence on pea yield-related traits and provide a foundation for future breeding programs aimed at improving productivity.

Keywords

Kaynakça

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  2. Assen, K. Y. (2020). Trait associations in prostrate and semi- leaf less type field pea (Pisum sativum L.) gene pools. American Journal of Environmental Sciences, 4, 54-60.
  3. Bai, G., Ge, Y., Hussain, W., Baenziger, P.S., & Graef, G. (2016). A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Computures and Electronics in Agriculture, 128, 181–192.
  4. Bijalwan, P., Raturi, A., Mishra, A. C. (2018). Character Association and Path Analysis Studies in Garden Pea (Pisum sativum L.) for Yield and Yield Attributes. International Journal of Current Microbiology and Applied Sciences, 7(3), 3491-3495.
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  7. Daniel, I. O., Adeboye, K. A., Oduwaye, O. O., & Porbeni, J. (2012). Digital seed morpho-metric characterization of tropical maize inbred lines for cultivar discrimination. International Journal of Plant Breeding and Genetics, 6(4), 245–251. Doi:10.3923/ijpbg.2012.245.251.
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Ayrıntılar

Kaynak Göster

APA
Waqas Ashiq, M., Sheraz, M., Shahnawaz, M., Zahoor, S., Aziz, A., Razzaq, S., Taimoor Farrukh Khan, M., Nawaz Khan, T., Yasir Malik, M., & Muzamil, M. (2025). Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis. Journal of Ecological Harmony, 1(2), 51-60. https://doi.org/10.5281/zenodo.17976793
AMA
1.Waqas Ashiq M, Sheraz M, Shahnawaz M, vd. Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis. Journal of Ecological Harmony. 2025;1(2):51-60. doi:10.5281/zenodo.17976793
Chicago
Waqas Ashiq, Muhammad, Muhammad Sheraz, Muhammad Shahnawaz, vd. 2025. “Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis”. Journal of Ecological Harmony 1 (2): 51-60. https://doi.org/10.5281/zenodo.17976793.
EndNote
Waqas Ashiq M, Sheraz M, Shahnawaz M, Zahoor S, Aziz A, Razzaq S, Taimoor Farrukh Khan M, Nawaz Khan T, Yasir Malik M, Muzamil M (01 Aralık 2025) Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis. Journal of Ecological Harmony 1 2 51–60.
IEEE
[1]M. Waqas Ashiq vd., “Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis”, Journal of Ecological Harmony, c. 1, sy 2, ss. 51–60, Ara. 2025, doi: 10.5281/zenodo.17976793.
ISNAD
Waqas Ashiq, Muhammad - Sheraz, Muhammad - Shahnawaz, Muhammad - Zahoor, Sidra - Aziz, Aamir - Razzaq, Sidra - Taimoor Farrukh Khan, Muhammad - Nawaz Khan, Tayyab - Yasir Malik, Muhammad - Muzamil, Muhammad. “Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis”. Journal of Ecological Harmony 1/2 (01 Aralık 2025): 51-60. https://doi.org/10.5281/zenodo.17976793.
JAMA
1.Waqas Ashiq M, Sheraz M, Shahnawaz M, Zahoor S, Aziz A, Razzaq S, Taimoor Farrukh Khan M, Nawaz Khan T, Yasir Malik M, Muzamil M. Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis. Journal of Ecological Harmony. 2025;1:51–60.
MLA
Waqas Ashiq, Muhammad, vd. “Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis”. Journal of Ecological Harmony, c. 1, sy 2, Aralık 2025, ss. 51-60, doi:10.5281/zenodo.17976793.
Vancouver
1.Muhammad Waqas Ashiq, Muhammad Sheraz, Muhammad Shahnawaz, Sidra Zahoor, Aamir Aziz, Sidra Razzaq, Muhammad Taimoor Farrukh Khan, Tayyab Nawaz Khan, Muhammad Yasir Malik, Muhammad Muzamil. Integrating Image-Based Phenotyping: A Comprehensive Analysis Using PCA, Cluster Analysis, and Path Coefficient Analysis. Journal of Ecological Harmony. 01 Aralık 2025;1(2):51-60. doi:10.5281/zenodo.17976793

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