A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation

İkbal Aygün; Onur Ümit Şener; Ulaş Özbent; Selami Beyhan

doi:10.15832/ankutbd.1795811

A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation

Abstract

In this paper, a new image filtering method is proposed using nonlinear symbolic functions. In this way, feature extraction from images is achieved using a small number of filters and parameters. Nonlinear symbolic functions are determined by an optimization method and different approaches are proposed to increase the filtering speed. Nonlinear functions also act as a regression layer and more efficient design is achieved by moving the image to nonlinear space. In sustainable agriculture, artificial intelligence has been recently utilized to pre-detect diseases and determine their severity to perform spraying more accurately in site-specific applications for human health and soil ingredients. Therefore, a new dataset for detecting the disease root rot severity of Arugula (Eruca vesicaria) was created and labeled for the image classification such that there is a significant decrease in production yield every year due to root rot disease. The proposed CNN model with nonlinear symbolic function filtering was produced %86.92 accuracy for the classification of disease severity whereas conventional 4-layer CNN model and pre-trained large image classification models such as VGG16, VGG19, ResNet50, ResNet101, InceptionV3, DenseNet, EfficientNetV2S, MobileNet, MobileNetV2 and Xception have been fine-tuned and classification accuracy is provided between 57-92% without preprocessing of images. Between the designed models, MobileNet model with less computational time and high accuracy can be a potential for future application in drone-based spraying systems. However, the proposed model with less number of parameters is also promising for future applications. The application results of image classification facilitate variable-rate spraying for sustainable agricultural production, ultimately protecting both human and soil health.

Keywords

Project Number

TUBITAK-1002 Research Projects with 124E079 grant number

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Agricultural Automatization

Journal Section

Research Article

Authors

İkbal Aygün ^*
0000-0003-1144-913X
Türkiye

Onur Ümit Şener This is me
0009-0005-0561-3687
Türkiye

Ulaş Özbent This is me
0009-0001-0339-0628
Türkiye

Selami Beyhan
0000-0002-9581-2794
Türkiye

Publication Date

October 12, 2026

Submission Date

October 3, 2025

Acceptance Date

May 14, 2026

Published in Issue

Year 2026 Volume: 32 Number: 4

DOI

https://doi.org/10.15832/ankutbd.1795811

IZ

https://izlik.org/JA66TW39SK

Cite

RIS / Bibtex

APA

Aygün, İ., Şener, O. Ü., Özbent, U., & Beyhan, S. (2026). A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation. Journal of Agricultural Sciences, 32(4), 1051-1061. https://doi.org/10.15832/ankutbd.1795811

AMA

1.Aygün İ, Şener OÜ, Özbent U, Beyhan S. A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation. J Agr Sci-Tarim Bili. 2026;32(4):1051-1061. doi:10.15832/ankutbd.1795811

Chicago

Aygün, İkbal, Onur Ümit Şener, Ulaş Özbent, and Selami Beyhan. 2026. “A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation”. Journal of Agricultural Sciences 32 (4): 1051-61. https://doi.org/10.15832/ankutbd.1795811.

EndNote

Aygün İ, Şener OÜ, Özbent U, Beyhan S (October 1, 2026) A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation. Journal of Agricultural Sciences 32 4 1051–1061.

IEEE

[1]İ. Aygün, O. Ü. Şener, U. Özbent, and S. Beyhan, “A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation”, J Agr Sci-Tarim Bili, vol. 32, no. 4, pp. 1051–1061, Oct. 2026, doi: 10.15832/ankutbd.1795811.

ISNAD

Aygün, İkbal - Şener, Onur Ümit - Özbent, Ulaş - Beyhan, Selami. “A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation”. Journal of Agricultural Sciences 32/4 (October 1, 2026): 1051-1061. https://doi.org/10.15832/ankutbd.1795811.

JAMA

1.Aygün İ, Şener OÜ, Özbent U, Beyhan S. A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation. J Agr Sci-Tarim Bili. 2026;32:1051–1061.

MLA

Aygün, İkbal, et al. “A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation”. Journal of Agricultural Sciences, vol. 32, no. 4, Oct. 2026, pp. 1051-6, doi:10.15832/ankutbd.1795811.

Vancouver

1.İkbal Aygün, Onur Ümit Şener, Ulaş Özbent, Selami Beyhan. A Novel Image Filtering Approach for Image Classification Applied to Severity Assessment of Root Rot Disease in Arugula Cultivation. J Agr Sci-Tarim Bili. 2026 Oct. 1;32(4):1051-6. doi:10.15832/ankutbd.1795811