Early diagnosis of plant diseases is of vital importance since they cause social, ecological, and economic losses. Therefore, it is highly complex and causes excessive workload and time loss. Within the scope of this article, nine tomato plant leaf diseases as well as healthy ones were classified using deep learning with new ensemble architectures. A total of 18.160 images were used for this process. In this study, in addition to the proposed two new convolutional neural networks (CNN) models, four other well-known CNN models (MobileNetV3Small, EfficientNetV2L, InceptionV3 and MobileNetV2) are used. A fine-tuning method is applied to the newly proposed CNNs models and then hyperparameter optimization is performed with the particle swarm optimization algorithm (PSO). Then, the weights of these architectures are optimized by the grid search method and triple and quintuple ensemble models are created and the datasets are classified with the help of the five-fold cross-validation. The experimental results demonstrate that the proposed ensemble models stand out with their fast training and testing time and superior classification performances with an accuracy of 99.60%. This research will help experts enable the early detection of plant diseases in a simple and quick manner and prevent the formation of new infections.

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In this work, a novel, to the best of our knowledge, metamaterial-based microwave sensor is designed, numerically simulated, and experimentally tested for milk and dairy products in the frequency range of 8 to 9 GHz. The proposed structure is composed of copper split-ring resonators printed on Arlon Diclad 527 dielectric substrate. Reflection coefficient S 11 was determined by using numerical simulation, and the structure was experimentally tested to validate the sensor at the X band frequency. The material under the test was placed in the sensor layer just behind the proposed structure, and the design was optimized to sense the change in the dielectric constant via resonance frequency shifts. The proposed study was not only used for fat contents and freshness checking of milk, it was also applied to other dairy products such as cheese, ayran, and yogurt. The maximum resonance frequency shift was observed in yogurt to be 140 MHz, and the minimum frequency shift was observed in fresh and spoiled ayran to be around 40 MHz. This work provides a new approach to the current metamaterial sensor studies existing in literature by having novel material applications with new microwave metamaterial sensors.

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Pneumonia causes the death of many children every year and constitutes a certain proportion of the world population. Chest X-rays are primarily used to diagnose this disease, but even for a trained radiologist, chest X-rays are not easy to interpret. In this study, a model for pneumonia detection trained on digital chest X-ray images is presented to assist radiologists in their decision-making processes. The study is carried out on the Phyton platform by using deep learning models, which have been widely preferred recently. In this study, a deep learning framework for pneumonia classification with four different CNN models is proposed. Three of them are pre-trained models, MobileNet, ResNet and AlexNet and the other is the recommended CNN Model. These models are evaluated by comparing them with each other according to their performance. The experimental performance of the proposed deep learning framework is evaluated on the basis of precision, recall and F1-score. The models achieved accuracy values of 93%, 97%, 97% and 86%, respectively. It is clear that the proposed ResNet model achieves the highest results compared to the others.