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Dergiler / Tarım Bilimleri Dergisi / 2026 / Volume: 32 Issue: 4

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

İkbal Aygün(EGE ÜNİVERSİTESİ)0000-0003-1144-913X
Onur Ümit Şener(İZMİR DEMOKRASİ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0009-0005-0561-3687
Ulaş Özbent(İZMİR DEMOKRASİ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0009-0001-0339-0628
Selami Beyhan(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0002-9581-2794
Sayfa
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Yayın Tarihi
2026-10-12
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The application results of image classification facilitate variable-rate spraying for sustainable agricultural production, ultimately protecting both human and soil health.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In this paper, a new image filtering method is proposed using nonlinear symbolic functions.
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Temel Bulgular & Veri:Nonlinear symbolic functions are determined by an optimization method and different approaches are proposed to increase the filtering speed.
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Sonuç & Katkı:The application results of image classification facilitate variable-rate spraying for sustainable agricultural production, ultimately protecting both human and soil health.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Symbolic functions · Nonlinear Filters · Root rot disease

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.

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Bu Makaleye Atıf Yap

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. Tarım Bilimleri Dergisi, Volume: 32 Issue: 4. https://doi.org/10.15832/ankutbd.1795811

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