Plant disease and pest detection using deep learning-based features

Muammer TÜRKOĞLU; Davut HANBAY

Makale detayları

Plant disease and pest detection using deep learning-based features

Yazarlar:

Muammer TÜRKOĞLU | Davut HANBAY

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 3, pp.1636-1651

Belge Tipi:

Makale

Yayın Tarihi:

5/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000733932

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers.

Makale detayları

Plant disease and pest detection using deep learning-based features

Yazarlar: Muammer TÜRKOĞLU | Davut HANBAY

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 3, pp.1636-1651

Belge Tipi: Makale

Yayın Tarihi: 5/2019

Belge Dili: Türkçe

Document ID (Org No.): 2000733932

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Muammer TÜRKOĞLU | Davut HANBAY

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 3, pp.1636-1651

Belge Tipi:

Makale

Yayın Tarihi:

5/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000733932

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632