Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

KOÇ C., Gerdan D., EMİNOĞLU M. B., YEGÜL U., Koc B., VATANDAŞ M.

NOTULAE BOTANICAE HORTI AGROBOTANICI CLUJ-NAPOCA, cilt.48, sa.4, ss.2316-2327, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48 Sayı: 4
  • Basım Tarihi: 2020
  • Doi Numarası: 10.15835/48412041
  • Dergi Adı: NOTULAE BOTANICAE HORTI AGROBOTANICI CLUJ-NAPOCA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Aqualine, CAB Abstracts, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.2316-2327
  • Anahtar Kelimeler: cultivar classification, DL4J, ensemble learning, hazelnut, machine learning
  • Ankara Üniversitesi Adresli: Evet

Özet

Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars ('Sivri', 'Kara', 'Tombul') using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.