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Journals / Black Sea Journal of Agriculture / 2026 / Cilt: 9 Sayı: 3

Convolutional Neural Networks for Multi-Class Wheat Fungal Disease Diagnosis: A Comparative Study of Transfer Learning Models

Cihat Gedik(BILECIK SEYH EDEBALI UNIVERSITY)0000-0003-4955-2220
Bahadır Sayıncı(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0001-7148-0855
Taner Yıldız(ONDOKUZ MAYIS ÜNİVERSİTESİ)0000-0002-4774-6534
Pages
332–344
Publication Date
2026-05-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results showed that DenseNet169 achieved the highest classification accuracy (86.87%), followed by DenseNet121 (84.16%) and MobileNet (81.07%).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Wheat (Triticum aestivum L.) is a staple food for billions worldwide and plays a critical role in global food security.
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Key Findings & Data:However, fungal pathogens cause yield losses and significant economic damage, posing a serious threat to wheat production.
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Conclusion & Impact:Results showed that DenseNet169 achieved the highest classification accuracy (86.87%), followed by DenseNet121 (84.16%) and MobileNet (81.07%).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Wheat diseases · Convolutional neural networks · Transfer learning

Abstract

Wheat (Triticum aestivum L.) is a staple food for billions worldwide and plays a critical role in global food security. However, fungal pathogens cause yield losses and significant economic damage, posing a serious threat to wheat production. Therefore, early and accurate detection is strategically important to mitigate these losses. In this study, a dataset comprising 5,325 images of seven major wheat fungal diseases Blast, Brown Rust, Stripe Rust, Fusarium Head Blight, Loose Smut, Powdery Mildew, and Septoria along with healthy samples was used. The images were split into training (70%), validation (15%), and test (15%) subsets, and four pre-trained convolutional neural network (CNN) models, MobileNet, NASNetMobile, DenseNet121 and DenseNet169 were fine-tuned using transfer learning. Results showed that DenseNet169 achieved the highest classification accuracy (86.87%), followed by DenseNet121 (84.16%) and MobileNet (81.07%). NASNetMobile, however, demonstrated the lowest performance during validation. The findings highlight the strong potential of DenseNet169 in achieving high accuracy for wheat disease classification and its applicability in developing early detection systems that support sustainable agricultural production.

Özet

Wheat (Triticum aestivum L.) is a staple food for billions worldwide and plays a critical role in global food security. However, fungal pathogens cause yield losses and significant economic damage, posing a serious threat to wheat production. Therefore, early and accurate detection is strategically important to mitigate these losses. In this study, a dataset comprising 5,325 images of seven major wheat fungal diseases Blast, Brown Rust, Stripe Rust, Fusarium Head Blight, Loose Smut, Powdery Mildew, and Septoria along with healthy samples was used. The images were split into training (70%), validation (15%), and test (15%) subsets, and four pre-trained convolutional neural network (CNN) models, MobileNet, NASNetMobile, DenseNet121 and DenseNet169 were fine-tuned using transfer learning. Results showed that DenseNet169 achieved the highest classification accuracy (86.87%), followed by DenseNet121 (84.16%) and MobileNet (81.07%). NASNetMobile, however, demonstrated the lowest performance during validation. The findings highlight the strong potential of DenseNet169 in achieving high accuracy for wheat disease classification and its applicability in developing early detection systems that support sustainable agricultural production.

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Cite This Article

Gedik, C., Sayıncı, B., Yıldız, T. (2026). Convolutional Neural Networks for Multi-Class Wheat Fungal Disease Diagnosis: A Comparative Study of Transfer Learning Models. Black Sea Journal of Agriculture, Cilt: 9 Sayı: 3, 332–344. https://doi.org/10.47115/bsagriculture.1836017

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