Classification and Analysis of Agaricus bisporus Diseases with Pre-Trained Deep Learning Models
Yazarlar (6)
Dr. Öğr. Üyesi Ümit Albayrak Selçuk Üniversitesi, Türkiye
Doç. Dr. Adem Gölcük Selçuk Üniversitesi, Türkiye
Prof. Dr. Sinan Aktaş Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Uğur Coruh Recep Tayyip Erdogan University, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
Doç. Dr. Ömer Kaan Baykan Konya Technical University, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Agronomy (Q1) | ||
| Dergi ISSN | 2073-4395 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 17-01-2025 |
| Cilt / Sayı / Sayfa | 15 / 1 / 226–0 | DOI | 10.3390/agronomy15010226 |
| Makale Linki | https://www.mdpi.com/2073-4395/15/1/226 | ||
| UAK Araştırma Alanları |
Matematiğin Temelleri ve Matematiksel Mantık
Topoloji
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| Özet |
| This research evaluates 20 advanced convolutional neural network (CNN) architectures for classifying mushroom diseases in Agaricus bisporus, utilizing a custom dataset of 3195 images (2464 infected and 731 healthy mushrooms) captured under uniform white-light conditions. The consistent illumination in the dataset enhances the robustness and practical usability of the assessed models. Using a weighted scoring system that incorporates precision, recall, F1-score, area under the ROC curve (AUC), and average precision (AP), ResNet-50 achieved the highest overall score of 99.70%, demonstrating outstanding performance across all disease categories. DenseNet-201 and DarkNet-53 followed closely, confirming their reliability in classification tasks with high recall and precision values. Confusion matrices and ROC curves further validated the classification capabilities of the models. These findings underscore the potential of CNN-based approaches for accurate and efficient early detection of mushroom diseases, contributing to more sustainable and data-driven agricultural practices. |
| Anahtar Kelimeler |
| Agaricus bisporus | convolutional neural networks | deep learning | image processing | mushroom diseases | precision agriculture | smart farming |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 5 |
| Scopus | 9 |
| Google Scholar | 12 |