An effective feature extraction method for olive peacock eye leaf disease classification

Diker, Aykut; Elen, Abdullah; Közkurt, Cemil; Kılıçarslan, Serhat; Dönmez, Emrah; Arslan, Kürşad; Kuran, Emre Can

doi:10.1007/s00217-023-04386-8

Cited by 5 publications

References 26 publications

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Classification of hazelnut varieties based on bigtransfer deep learning model

Dönmez,

Kılıçarslan,

Diker

2024

Eur Food Res Technol

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Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 × 1, BiT-M R101 × 3 and BiT-M R152 × 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 × 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country.

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Classification of hazelnut varieties based on bigtransfer deep learning model

Dönmez,

Kılıçarslan,

Diker

2024

Eur Food Res Technol

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Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+

Dönmez

2024

Eur Food Res Technol

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Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated.

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Impact of optimizers functions on detection of Melanoma using transfer learning architectures

Kılıçarslan,

Aydın,

Adem

et al. 2024

Multimed Tools Appl

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Early diagnosis-treatment of melanoma is very important because of its dangerous nature and rapid spread. When diagnosed correctly and early, the recovery rate of patients increases significantly. Physical methods are not sufficient for diagnosis and classification. The aim of this study is to use a hybrid method that combines different deep learning methods in the classification of melanoma and to investigate the effect of optimizer methods used in deep learning methods on classification performance. In the study, Melanoma detection was carried out from the skin lesions image through a simulation created with the deep learning architectures DenseNet, InceptionV3, ResNet50, InceptionResNetV2 and MobileNet and seven optimizers: SGD, Adam, RmsProp, AdaDelta, AdaGrad, Adamax and Nadam. The results of the study show that SGD has better and more stable performance in terms of convergence rate, training speed and performance than other optimizers. In addition, the momentum parameter added to the structure of the SGD optimizer reduces the oscillation and training time compared to other functions. It was observed that the best melanoma detection among the combined methods was achieved using the DenseNet model and SGD optimizer with a test accuracy of 0.949, test sensitivity 0.9403, and test F score 0.9492.

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An effective feature extraction method for olive peacock eye leaf disease classification

Cited by 5 publications

References 26 publications

Classification of hazelnut varieties based on bigtransfer deep learning model

Classification of hazelnut varieties based on bigtransfer deep learning model

Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+

Impact of optimizers functions on detection of Melanoma using transfer learning architectures

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