International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Optimizing Feature Extraction for Machine Learning Classification of Corn Seed Varieties Using SVM

Author(s) Mr. Ramalinga Reddy S, Dr. Suma a R
Country India
Abstract optimized feature extraction of corn seed varieties is crucial for various agricultural applications, including quality control, crop breeding, and precision farming. This study presents an approach to optimizing feature extraction techniques for the machine learning-based classification of corn seed varieties using Support Vector Machines (SVM). The performance of the SVM classifier heavily depends on the quality of features extracted from the seed images or data. In this work, we propose a hybrid feature extraction strategy that combines traditional methods such as texture, shape, and color analysis with advanced dimensionality reduction techniques. We optimize the feature selection process using genetic algorithms and feature importance analysis to identify the most discriminative features. The optimized feature set is then fed into an SVM classifier, where the kernel parameters and hyperparameters are fine-tuned through grid search and cross-validation. Our results demonstrate a significant improvement in classification accuracy compared to baseline models that use unoptimized feature sets. The optimized SVM model achieves higher precision, recall, and F1 score, showcasing the effectiveness of the proposed feature extraction and optimization strategy. This work provides a robust framework for accurate corn seed variety classification, with potential applications in automated seed sorting and quality assessment systems
Keywords Corn Seed Classification, Feature Extraction, Support Vector Machine (SVM), Machine Learning, Dimensionality Reduction, Genetic Algorithm, Feature Selection, Image Analysis, Agricultural Technology, Optimization
Field Engineering
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.41185
Short DOI https://doi.org/g9fm4n
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E-ISSN 2582-2160
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