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Plant disease and pest detection using deep learning-based features

MUAMMER TÜRKOĞLU [1] , DAVUT HANBAY [2]

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.
Plant disease and pest detection, convolutional neural networks, deep learning architectures, feature extraction, classifier methods
Journal Section Articles Author: MUAMMER TÜRKOĞLU Author: DAVUT HANBAY Publication Date : June 1, 2019
 Bibtex @ { tbtkelektrik577521, journal = {Turkish Journal of Electrical Engineering and Computer Science}, issn = {1300-0632}, eissn = {1303-6203}, address = {}, publisher = {TUBITAK}, year = {2019}, volume = {27}, pages = {1636 - 1651}, doi = {}, title = {Plant disease and pest detection using deep learning-based features}, key = {cite}, author = {TÜRKOĞLU, MUAMMER and HANBAY, DAVUT} } APA TÜRKOĞLU, M , HANBAY, D . (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering and Computer Science , 27 (3) , 1636-1651 . Retrieved from https://dergipark.org.tr/en/pub/tbtkelektrik/issue/45742/577521 MLA TÜRKOĞLU, M , HANBAY, D . "Plant disease and pest detection using deep learning-based features". Turkish Journal of Electrical Engineering and Computer Science 27 (2019 ): 1636-1651 Chicago TÜRKOĞLU, M , HANBAY, D . "Plant disease and pest detection using deep learning-based features". Turkish Journal of Electrical Engineering and Computer Science 27 (2019 ): 1636-1651 RIS TY - JOUR T1 - Plant disease and pest detection using deep learning-based features AU - MUAMMER TÜRKOĞLU , DAVUT HANBAY Y1 - 2019 PY - 2019 N1 - DO - T2 - Turkish Journal of Electrical Engineering and Computer Science JF - Journal JO - JOR SP - 1636 EP - 1651 VL - 27 IS - 3 SN - 1300-0632-1303-6203 M3 - UR - Y2 - 2019 ER - EndNote %0 Turkish Journal of Electrical Engineering and Computer Science Plant disease and pest detection using deep learning-based features %A MUAMMER TÜRKOĞLU , DAVUT HANBAY %T Plant disease and pest detection using deep learning-based features %D 2019 %J Turkish Journal of Electrical Engineering and Computer Science %P 1300-0632-1303-6203 %V 27 %N 3 %R %U ISNAD TÜRKOĞLU, MUAMMER , HANBAY, DAVUT . "Plant disease and pest detection using deep learning-based features". Turkish Journal of Electrical Engineering and Computer Science 27 / 3 (June 2019): 1636-1651 . AMA TÜRKOĞLU M , HANBAY D . Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering and Computer Science. 2019; 27(3): 1636-1651. Vancouver TÜRKOĞLU M , HANBAY D . Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering and Computer Science. 2019; 27(3): 1651-1636.