Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Classification of Garlic Varieties with Fluorescent Spectroscopy Using Machine Learning
Tehnicki Glasnik · Ocak 2024
YÖKSİS Kayıtları
Classification of Garlic Varieties with Fluorescent Spectroscopy Using Machine Learning
TEHNICKI GLASNIK-TECHNICAL JOURNAL · 2024
DOÇENT ALİ YAŞAR →
Makale Bilgileri
DergiTehnicki Glasnik
Yayın TarihiOcak 2024
Cilt / Sayfa18 · 523-531
Scopus ID2-s2.0-85208377155
Erişim🔓 Açık Erişim
Özet
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.
Yazarlar (3)
1
Ali Yasar
2
Vanya Slavova
3
Stefka Genova
Anahtar Kelimeler
Fluorescence Spectroscopic Data
Garlic
Hyper Parameter Tuning
Machine Learning Algorithms
Performance Metrics
Kurumlar
Maritsa Vegetable Crops Research Institute, Plovdiv
Plovdiv Bulgaria
Selçuk Üniversitesi
Selçuklu Turkey