Performance of vision transformer and swin transformer models for lemon quality classification in fruit juice factories

Dümen S., Kavalcı Yılmaz E., Adem K., Avaroglu E.

European Food Research and Technology, cilt.250, sa.9, ss.2291-2302, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 250 Sayı: 9
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00217-024-04537-5
  • Dergi Adı: European Food Research and Technology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Compendex, Food Science & Technology Abstracts, Hospitality & Tourism Complete, Hospitality & Tourism Index, Veterinary Science Database
  • Sayfa Sayıları: ss.2291-2302
  • Anahtar Kelimeler: Deep learning, Lemon quality, Swin transformer, Vision transformer
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Assessing the quality of agricultural products holds vital significance in enhancing production efficiency and market viability. The adoption of artificial intelligence (AI) has notably surged for this purpose, employing deep learning and machine learning techniques to process and classify agricultural product images, adhering to defined standards. This study focuses on the lemon dataset, encompassing ‘good’ and ‘bad’ quality classes, initiate by augmenting data through rescaling, random zoom, flip, and rotation methods. Subsequently, employing eight diverse deep learning approaches and two transformer methods for classification, the study culminated in the ViT method achieving an unprecedented 99.84% accuracy, 99.95% recall, and 99.66% precision, marking the highest accuracy documented. These findings strongly advocate for the efficacy of the ViT method in successfully classifying lemon quality, spotlighting its potential impact on agricultural quality assessment.