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.

In recent years, significant advancements in artificial intelligence, particularly in the field of deep learning, have increasingly been integrated into agricultural applications, including critical processes such as disease detection. Tomato, being one of the most widely consumed agricultural products globally and highly susceptible to a variety of fungal, bacterial, and viral pathogens, remains a prominent focus in disease detection research. In this study, we propose a deep learning-based approach for the detection of tomato diseases, a critical challenge in agriculture due to the crop’s vulnerability to fungal, bacterial, and viral pathogens. We constructed an original dataset comprising 6414 images captured under real production conditions, categorized into three image types: leaves, green tomatoes, and red tomatoes. The dataset includes five classes: healthy samples, late blight, early blight, gray mold, and bacterial cancer. Twenty-one deep learning models were evaluated, and the top five performers (EfficientNet-b0, NasNet-Large, ResNet-50, DenseNet-201, and Places365-GoogLeNet) were selected for feature extraction. From each model, 1000 deep features were extracted, and feature selection was conducted using MRMR, Chi-Square (Chi²), and ReliefF methods. The top 100 features from each selection technique were then used for reclassification with traditional machine learning classifiers under five-fold cross-validation. The highest test accuracy of 92.0% was achieved with EfficientNet-b0 features, Chi² selection, and the Fine KNN classifier. EfficientNet-b0 consistently outperformed other models, while the combination of NasNet-Large and Wide Neural Network yielded the lowest performance. These results demonstrate the effectiveness of combining deep learning-based feature extraction with traditional classifiers and feature selection techniques for robust detection of tomato diseases in real-world agricultural environments.

Bu makale, Agaricus bisporus (J.E. Lange) Imbach’un kültüründe görülen hastalıkların sınıflandırması için görüntü tabanlı bir veri seti oluşturulması ve analiz edilmesi üzerine yapılan bir araştırmayı ele almaktadır. Veri seti, sağlıklı ve farklı hastalık sınıflarına ait görüntüleri içermektedir. Farklı aydınlatma koşullarında elde edilen görüntüler, ayrı bir sınıflandırma problemi için kullanılabilecek uygunlukta veriler sunmaktadır. Bu araştırma, mantar hastalıklarının tanımlanması ve sınıflandırılması için kullanılabilecek bir veri setinin oluşturulması, hastalıkların otomatik olarak tanımlanması ve sınıflandırılmasını mümkün kılacak derin öğrenme veya diğer makine öğrenmesi tekniklerinin kullanılmasına imkân sağlayacaktır. Veri setinin oluşturulması sürecinde, çalışma kapsamında geliştirilmiş olan taşınabilir mantar görüntüleme sistemi ile mantar işletmeleri ziyaretleri gerçekleştirilmiş; yaklaşık 7250 adet hastalıklı mantar, 1800 adet de sağlıklı mantar görüntüsü elde edilmiştir (Her bir aydınlatma ortamı için yaklaşık 3000 adet). Kültür mantarlarında yaygın görülen 4 farklı sınıf hastalık gözlemlenmiştir. Her bir mantar 3 farklı aydınlatma ortamında görüntülenmiştir.