A comparative study of bread wheat varieties identification on feature extraction, feature selection and machine learning algorithms

Kılıçarslan, Serhat; Kılıçarslan, Sabire

doi:10.1007/s00217-023-04372-0

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…Therefore, this paper realizes the extraction of color features of architectural interior space with the help of evolutionary deep learning algorithm. The convergence threshold of evolutionary deep learning is set to satisfy [24]:…”

Section: Feature Extraction Implementation Based On Evolutionary Deep...mentioning

confidence: 99%

A Deep Learning Model-Based Feature Extraction Method for Architectural Space and Color Connection in Interior Design

Liang,

Xiao,

Guo

2024

SCPE

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In architectural interior design, color is one of the important design elements. Through the reasonable combination of various color elements, it can effectively improve the interior environment and create an atmosphere that meets the preferences and needs of users. And with the continuous development of social economy, the application of color in interior design is becoming more and more widespread. Using different colors in interior design to harmonize not only can relieve people’s visual fatigue, but also can bring people a pleasant mood. Different colors have different meanings, therefore, the use of color in interior design should be more flexible and color matching should be more innovative. The warm and cold, near and far, expansion and contraction of color make the color space the most dynamic key element in design. The grasp of color and scale of architectural space and the flexible use of color will directly affect the quality of architectural space design. Color can strengthen the form of interior space or destroy its form. In order to accurately grasp the connection between architectural space and color in interior design, this paper proposes a deep learning model-based feature extraction method for the connection between architectural space and color in interior design. First, we construct a product color sentiment imagery dataset; then, we build a model for generating architectural interior space layout and color design schemes based on the product color sentiment imagery dataset and conditional deep convolutional generation adversarial network, and innovatively generate product color design schemes. This algorithm can better balance the chromaticity, saturation, and clarity of images. When determining the similarity of indoor space colors, depth features are superior to point-to-point pixel distance and aesthetic features of indoor space colors. Finally, the effectiveness and applicability of the proposed method are verified in relevant experiments.

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Section: Feature Extraction Implementation Based On Evolutionary Deep...mentioning

confidence: 99%

A Deep Learning Model-Based Feature Extraction Method for Architectural Space and Color Connection in Interior Design

Liang,

Xiao,

Guo

2024

SCPE

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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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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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A comparative study of bread wheat varieties identification on feature extraction, feature selection and machine learning algorithms

Cited by 7 publications

References 42 publications

A Deep Learning Model-Based Feature Extraction Method for Architectural Space and Color Connection in Interior Design

A Deep Learning Model-Based Feature Extraction Method for Architectural Space and Color Connection in Interior Design

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+

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