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Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds.

Sıtkı Ermiş1, Uğur Ercan2, Aylin Kabaş3

  • 1Department of Horticulture, Faculty of Agriculture, Eskişehir Osmangazi University, Eskişehir 26040, Türkiye.

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Summary

Machine learning accurately classifies ornamental pumpkin seeds using morphological and colorimetric data. The Random Forest model achieved 95.9% accuracy, improving seed industry efficiency and breeding programs.

Keywords:
artificial intelligencemachine learningmorphological analysisornamental pumpkinseed classification

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Ornamental pumpkin (Cucurbita pepo L. var. ovifera) seeds exhibit significant morphological variability, complicating classification.
  • Accurate seed classification is crucial for agricultural production, breeding, and commercial seed sorting.
  • Existing classification methods may lack the efficiency and accuracy required for the seed industry.

Purpose of the Study:

  • To employ machine learning models for classifying ornamental pumpkin seeds based on physical and color attributes.
  • To evaluate the performance of Random Forest (RF), LightGBM, and k-Nearest Neighbors (KNN) algorithms.
  • To determine the most effective model for automated seed classification in the ornamental pumpkin industry.

Main Methods:

  • Collected morphological (mass, elongation, width, thickness) and colorimetric (CIELAB L*, a*, b*) data from 900 ornamental pumpkin seeds (six varieties).
  • Preprocessed the dataset using normalization and balancing techniques to optimize model training.
  • Trained and evaluated RF, LightGBM, and KNN models, assessing performance with metrics like Accuracy, Precision, Recall, F1 Score, MCC, and Cohen's Kappa.

Main Results:

  • The Random Forest (RF) model demonstrated superior performance, achieving 95.9% accuracy and a Matthews Correlation Coefficient (MCC) of 0.951.
  • RF model achieved high scores across all metrics: Balanced Accuracy (0.961), Precision (0.962), Recall (0.961), and F1 Score (0.961).
  • The k-Nearest Neighbors (KNN) model exhibited the lowest classification performance among the tested algorithms.

Conclusions:

  • Machine learning, particularly the Random Forest model, offers a highly accurate and efficient solution for automated ornamental pumpkin seed classification.
  • AI-driven classification can significantly minimize errors, enhance efficiency in seed sorting, and support the development of effective breeding schemes.
  • This approach holds substantial potential for advancing the ornamental pumpkin seed industry through improved quality control and selection processes.