The severity level classification of Fusarium wilt of chickpea by pre-trained deep learning models

Hayıt T., Endes A., Hayıt F.

JOURNAL OF PLANT PATHOLOGY, cilt.106, ss.93-105, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 106
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s42161-023-01520-z
  • Dergi Adı: JOURNAL OF PLANT PATHOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Environment Index
  • Sayfa Sayıları: ss.93-105
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

Fusarium wilt of chickpea causes significant losses in chickpea production worldwide due to its severely damaging characteristic. On the other hand, it can be controlled by growing resistant varieties, using fungicides, or by on-site applications. It is very important to detect the disease in the early stages before the disease is fully transmitted to the product in order to avoid the disruption of the production of chickpeas. This study investigated the applicability of pre-trained models based on deep learning, which could help determine the type of infection at an early stage of the Fusarium wilt of chickpea, with a proposed new dataset. The results of the study showed that pre-trained Convolutional Neural Network models could be used for classifying the disease. Models can classify the images of chickpea leaves used as inputs as "Highly Resistant, Resistant, Moderately Resistant/ Tolerant, Susceptible, and Highly Susceptible", according to the severity of the disease. Convolutional Neural Networks are among the state-of-the-art deep learning approaches and one of the approaches that are inspired by the human brain and can automatically learn the distinguishing features from the dataset. Therefore, they can perform close to expert performance in different tasks and applications. The study used a novel dataset containing images of chickpea plants infected by the pathogen Fusarium oxysporum f. sp. ciceris to train, validate, and test the pre-trained deep-learning models. According to the results of the study, DenseNet-201, with an average test accuracy of 90%, outperformed the other models. Furthermore, the confusion matrix of DenseNet-201 shows that the other metrics (precision, recall, and F1-Score) were consistent with the average test accuracy. The obtained accuracy value and other performance indicators indicate that pre-trained Convolutional Neural Network models can help determine the severity of Fusarium wilt in chickpea.