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https://doi.org/10.31803/tg-20230710121320

Classification of Garlic Varieties with Fluorescent Spectroscopy Using Machine Learning

Ali Yasar orcid.org/0000-0001-9012-7950 ; Department of Mechatronics Engineering, Selcuk University, Keykubat Kampüsü, Selçuklu/Konya, 42250, Türkiye *
Vanya Slavova ; Department of Breeding, Maritsa Vegetable Crops Research Institute, Agricultural Academy, Brezovsko shosse 32, 4000 Plovdiv, Bulgaria
Stefka Genova ; Department of Breeding, Maritsa Vegetable Crops Research Institute, Agricultural Academy, Brezovsko shosse 32, 4000 Plovdiv, Bulgaria

* Dopisni autor.

Sažetak

Machine learning techniques can produce fast, accurate and objective results in the analysis of agricultural products. These artificial intelligence-based systems are frequently encountered in studies on agriculture in the literature. This study reveals the usability of machine learning algorithms in classification of garlic cultivars using fluorescent spectroscopic data. For this, six types of garlic were used: Razgradski-11, Razgradski-12, Razgradski-115, Plovdivski-120, Yambolski-99 and Topolovgradski. In the first stage, the parsing analysis made from the fluorescent spectroscopic data of the garlics was carried out with seven different machine learning. The classification results of these seven types of machine learning algorithms were obtained. In the second stage, the classification results were obtained by adjusting the hyperparameters of each Machine Learning (ML) algorithm in order to control the improvability of the classification accuracy rates. Finally, performance metrics such as Specificity, precision, MCC, F1-Score of the classification processes obtained in the two stages were compared. In general, it was observed that the classification performances increased with the hyperparameter adjustment performed in the second stage. In this study, classification results with ML showed that fluorescent spectroscopy data of garlic strongly represented garlic species and provided high performance classification accuracy of 99.93% with Neural Network (NN), one of the machine learning methods using these data.

Ključne riječi

Fluorescence Spectroscopic Data; Garlic; Hyper Parameter Tuning; Machine Learning Algorithms; Performance Metrics

Hrčak ID:

321392

URI

https://hrcak.srce.hr/321392

Datum izdavanja:

5.12.2024.

Posjeta: 130 *