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<p>Almonds are an agricultural product with nutritious features. Almond species are important in terms of marketing and in terms of sustainability. Traditional almond classification methods can be time-consuming and expensive. In addition, Machine Learning (ML) methods stand out due to the tendency of human errors in the classification. ML methods can give both faster and more accurate results. This study aims to improve the performance of ML methods in the classification of almonds. In the study, almond data obtained from the data set were first processed with image processing methods. In this process, the noise and shadows in the images were removed. Then, 26 different features were extracted from the almond images. These extracted features were processed using SVM, RF, FCNN, LightGBM, CatBoost, and XGBoost ML methods in the classification process. It has provided accuracy, especially in SVM, FCCN, and XGBOOST models, and high values in metrics such as F1-Score. In the OptHO-SVM method, where Optuna was applied, the accuracy increased from 90.06 to 96.53%, while in the OptHO-FCNN method, the accuracy increased from 94.23 to 96.40%. In addition, significant improvements were observed in loss metrics such as Log Loss; Log Loss value improved by 58.75% in the OptHO-SVM method.</p>

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Classification of almond kernels with optuna hyper-parameter optimization using machine learning methods

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Hayri Incekara,
  • Inayet Hakkı Cizmeci,
  • Mücahid Mustafa Saritas,
  • Murat Koklu

摘要

Almonds are an agricultural product with nutritious features. Almond species are important in terms of marketing and in terms of sustainability. Traditional almond classification methods can be time-consuming and expensive. In addition, Machine Learning (ML) methods stand out due to the tendency of human errors in the classification. ML methods can give both faster and more accurate results. This study aims to improve the performance of ML methods in the classification of almonds. In the study, almond data obtained from the data set were first processed with image processing methods. In this process, the noise and shadows in the images were removed. Then, 26 different features were extracted from the almond images. These extracted features were processed using SVM, RF, FCNN, LightGBM, CatBoost, and XGBoost ML methods in the classification process. It has provided accuracy, especially in SVM, FCCN, and XGBOOST models, and high values in metrics such as F1-Score. In the OptHO-SVM method, where Optuna was applied, the accuracy increased from 90.06 to 96.53%, while in the OptHO-FCNN method, the accuracy increased from 94.23 to 96.40%. In addition, significant improvements were observed in loss metrics such as Log Loss; Log Loss value improved by 58.75% in the OptHO-SVM method.