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Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction.
Bhamidipati Kishore1, Ali Yasar2, Yavuz Selim Taspinar3
1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
Computational Intelligence and Neuroscience
|August 22, 2022
Summary
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.
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.
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