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Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction.

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This study developed an efficient corn seed classification system using deep learning and optimization algorithms. Machine learning models accurately distinguished corn varieties, offering faster and more cost-effective seed identification.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Machine Learning

Background:

  • Accurate corn seed classification is crucial for agriculture and animal feed due to the significance of seed quality and marketing.
  • Distinguishing between numerous corn varieties is essential for maintaining purity and market value.

Purpose of the Study:

  • To develop and evaluate a machine learning-based system for classifying four distinct corn seed varieties (BT6470, Calipso, Es_Armandi, Hiva).
  • To investigate the effectiveness of deep feature extraction combined with optimization algorithms for enhancing classification accuracy and efficiency.

Main Methods:

  • Deep feature extraction using the pretrained SqueezeNet convolutional neural network (CNN) model on 14,469 corn seed images.
  • Feature selection using Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms to reduce dimensionality.
  • Classification using machine learning models: Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN).

Main Results:

  • The multi-class Support Vector Machine (mSVM) achieved the highest initial classification accuracy of 89.40% using deep features from SqueezeNet.
  • Feature selection algorithms (BA, WOA, GWO) resulted in classification accuracies of 88.82%, 88.72%, and 88.95% respectively with mSVM, demonstrating comparable performance with reduced features.
  • Optimization algorithms enabled successful classification with fewer features and in a shorter processing time, indicating improved efficiency.

Conclusions:

  • The integration of deep learning and optimization algorithms provides an effective, objective, and time-efficient method for corn seed classification.
  • The proposed approach offers a valuable perspective for improving classification performance in agricultural applications, particularly for seed identification and quality control.