Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion

Murat Kürşat; Mücahit Karaduman; Hursit Burak Mutlu; İrfan Emre; Muhammed Yıldırım

doi:10.15832/ankutbd.1661676

Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion

Abstract

Content-based Image Retrieval (CBIR) systems have been used frequently in recent years, along with developing technology. Especially in large datasets, retrieval-based systems produce more successful results. This study created a dataset consisting of 27 different Euphorbia seed types belonging to the same genus. It is difficult for Convolutional Neural Network (CNN) architectures to produce successful results in the created dataset. In addition, the high computational and memory requirements of CNN architectures have further increased the need for CBIR systems in large datasets. Therefore, a hybrid retrieval system was developed to make inferences from 27 different seed images. In the developed system, feature extraction was performed using Darknet53, Xception, and Densenet201 architectures. These extracted features were concatenated to bring together different features of the same image. Then, unnecessary features were eliminated from the combined features with the Neighborhood Component Analysis (NCA) method. The cosine similarity measurement metric was used to measure the similarity between the query image and other images. Precision-recall curves and Average Precision (AP) metrics were used to measure the performance of the proposed retrieval-based system. In the study, an average AP value of 0.96809 was obtained. The morphology of the seeds is a critical characteristic of Euphorbia, and this work has validated the artificial intelligence methodology.

Keywords

References

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Details

Primary Language

English

Subjects

Knowledge Representation and Reasoning, Autonomous Agents and Multiagent Systems

Journal Section

Research Article

Authors

Murat Kürşat
0000-0002-0861-4213
Türkiye

Mücahit Karaduman
0000-0002-8087-4044
Türkiye

Hursit Burak Mutlu
0009-0009-2176-0192
Türkiye

İrfan Emre
0000-0003-0591-3397
Türkiye

Muhammed Yıldırım ^*
0000-0003-1866-4721
Türkiye

Publication Date

September 30, 2025

Submission Date

March 20, 2025

Acceptance Date

May 8, 2025

Published in Issue

Year 2025 Volume: 31 Number: 4

DOI

https://doi.org/10.15832/ankutbd.1661676

IZ

https://izlik.org/JA77MH36BW

Cite

RIS / Bibtex

APA

Kürşat, M., Karaduman, M., Mutlu, H. B., Emre, İ., & Yıldırım, M. (2025). Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion. Journal of Agricultural Sciences, 31(4), 931-940. https://doi.org/10.15832/ankutbd.1661676

AMA

1.Kürşat M, Karaduman M, Mutlu HB, Emre İ, Yıldırım M. Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion. J Agr Sci-Tarim Bili. 2025;31(4):931-940. doi:10.15832/ankutbd.1661676

Chicago

Kürşat, Murat, Mücahit Karaduman, Hursit Burak Mutlu, İrfan Emre, and Muhammed Yıldırım. 2025. “Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion”. Journal of Agricultural Sciences 31 (4): 931-40. https://doi.org/10.15832/ankutbd.1661676.

EndNote

Kürşat M, Karaduman M, Mutlu HB, Emre İ, Yıldırım M (September 1, 2025) Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion. Journal of Agricultural Sciences 31 4 931–940.

IEEE

[1]M. Kürşat, M. Karaduman, H. B. Mutlu, İ. Emre, and M. Yıldırım, “Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion”, J Agr Sci-Tarim Bili, vol. 31, no. 4, pp. 931–940, Sept. 2025, doi: 10.15832/ankutbd.1661676.

ISNAD

Kürşat, Murat - Karaduman, Mücahit - Mutlu, Hursit Burak - Emre, İrfan - Yıldırım, Muhammed. “Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion”. Journal of Agricultural Sciences 31/4 (September 1, 2025): 931-940. https://doi.org/10.15832/ankutbd.1661676.

JAMA

1.Kürşat M, Karaduman M, Mutlu HB, Emre İ, Yıldırım M. Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion. J Agr Sci-Tarim Bili. 2025;31:931–940.

MLA

Kürşat, Murat, et al. “Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion”. Journal of Agricultural Sciences, vol. 31, no. 4, Sept. 2025, pp. 931-40, doi:10.15832/ankutbd.1661676.

Vancouver

1.Murat Kürşat, Mücahit Karaduman, Hursit Burak Mutlu, İrfan Emre, Muhammed Yıldırım. Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion. J Agr Sci-Tarim Bili. 2025 Sep. 1;31(4):931-40. doi:10.15832/ankutbd.1661676