Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Ulutaş H., Emin Sahin M.

Journal of Electronic Imaging, cilt.32, sa.5, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 5
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1117/1.jei.32.5.053040
  • Dergi Adı: Journal of Electronic Imaging
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: convolutional neural network, cucumber disease, deep learning, ensemble, weighted average ensemble
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

Plant diseases are a major concern for farmers worldwide as they can cause significant economic losses. Among vegetable crops, cucumber is particularly susceptible to various diseases, including fusarium and mildew. We present a unique dataset of cucumber leaf images collected from the Carsamba and Silifke districts of Turkey. This dataset is used in both newly proposed convolutional neural networks and pre-trained models available in the literature in addition to new ensemble models for disease prediction and classification. To optimize the classification accuracy, the grid search method is employed to identify the best combinations of base models and ensemble learning methods. The classification process is carried out using newly designed triple ensemble models and utilized fivefold cross-validation, and the results are evaluated using accuracy, F1-score, and receiver operating characteristic curves. The highest accuracy rate achieved in our study is 93.79%, which demonstrates the effectiveness of our approach for accurately identifying cucumber leaf diseases. Our proposed approach is not only accurate but also effective, making it a practical solution for farmers to use to quickly identify and control diseases in their crops. Our study provides valuable insights into the application of deep learning techniques for the classification of cucumber plant diseases and can serve as a useful model for further research in this field.