Since many years ago, walnuts have been extensively available around the world and come in various quality varieties. The proper variety of walnut can be grown in the right area and is vital to human health. This fruit's production is time-consuming and expensive. However, even specialists find it challenging to differentiate distinct kinds since walnut leaves are so similar in color and feel. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The classification process can now be carried out automatically from leaf photos thanks to technological advancements. The walnut data set was applied to the suggested deep learning model. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The walnut data set, which consists of 18 different types of 1751 photos, was used to test the suggested deep learning model. The three most successful algorithms among the commonly utilized CNN algorithms in the literature were first selected for the suggested model. From the Vgg16, Vgg19, and AlexNet CNN algorithms, many features were retrieved. Utilizing the Whale Optimization Algorithm (WOA), a new feature set was produced by choosing the top extracted features. KNN is used to categorize this feature set. An accuracy rating of 92.59% was attained as a consequence of the tests.

In order to determine the variety from walnut leaves, each leaf must be examined in detail. Species that are very similar in color and shape to each other are very difficult to distinguish with the human eye. Examining and classifying plant leaves belonging to many classes one by one is not appropriate in terms of time and cost. Studies on walnut varieties in the literature are generally classified as a result of experimental studies in the laboratory environment. There are two or three different classes in studies using walnut leaf images. In this study, firstly, a unique walnut dataset obtained from 1751 walnut leaf images obtained from 18 different walnut varieties was created. Classification was made using deep learning methods on the original walnut dataset. It has been tested with CNN models, which are widely used in the literature, and some performance metrics are recorded and the results are compared. The images were first preprocessed for cropping, denoising and resizing. Classification was made using CNN models on the original dataset and augmented dataset with data augmentation method. It was seen that the VGG16 CNN model gave the best results both in the original dataset and the augmented dataset. In this model, the accucarcy result found with the original data set was 0.8552, while the accuracy result in the enhanced data set was 0.9055. When the accuracy values are examined, it is seen that walnut varieties are classified successfully.

Maize is a vital global crop, serving as a fundamental component of global food security. To support sustainable maize production, the accurate classification of maize seeds—particularly distinguishing haploid from diploid types—is essential for enhancing breeding efficiency. Conventional methods relying on manual inspection or simple machine learning are prone to errors and unsuitable for large-scale data. To overcome these limitations, we propose Slot-Maize, a novel deep learning architecture that integrates Convolutional Neural Networks (CNN), Slot Attention, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) layers. The Slot-Maize model was evaluated using two datasets: the Maize Seed Dataset and the Maize Variety Dataset. The Slot Attention module improves feature representation by focusing on object-centric regions within seed images. The GRU captures short-term sequential patterns in extracted features, while the LSTM models long-range dependencies, enhancing temporal understanding. Furthermore, Grad-CAM was utilized as an explainable AI technique to enhance the interpretability of the model’s decisions. The model demonstrated an accuracy of 96.97% on the Maize Seed Dataset and 92.30% on the Maize Variety Dataset, outperforming existing methods in both cases. These results demonstrate the model’s robustness, generalizability, and potential to accelerate automated maize breeding workflows. In conclusion, the Slot-Maize model provides a robust and interpretable solution for automated maize seed classification, representing a significant advancement in agricultural technology. By combining accuracy with explainability, Slot-Maize provides a reliable tool for precision agriculture.

Melanoma is one of the most aggressive and lethal forms of skin cancer. Therefore, early diagnosis and correct diagnosis are very important for the health of the patient. Diagnostic procedures require human expertise, increasing the possibility of error. With developing technology, advances in deep learning models have become hope for the automatic detection of Melanoma skin cancer with computer systems. The Vision Transformer (ViT) model was developed by Google and has achieved very successful results in the field of classification. In this study, the transfer learning method was applied with the ViT model using the melanoma skin cancer dataset taken from the Kaggle library and the performance of the model was evaluated. Before starting training, pre-processing was applied to the data set. The dataset consists of 9600 training and 1000 test images. Training and experimental testing of the model was carried out with Python language on the Colab platform. As a result of the experimental studies conducted on the test data set, it was seen that the model reached an accuracy rate of 93.5% and was competitive with existing models

Walnut is a widely cultivated crop with various types and qualities, offering significant health benefits. However, its long production cycle and high cultivation costs necessitate the selection of appropriate varieties for specific ecological conditions. Due to morphological and color similarities, differentiating walnut varieties remains challenging, even for experts. Existing studies on walnut classification are limited and mostly confined to laboratory-based experiments. In this study, a novel hybrid computer-based approach is proposed for the automatic classification of walnut varieties using leaf images. A dataset consisting of 1,751 images from 18 different walnut varieties was collected from the Atatürk Horticultural Central Research Institute in Yalova, Turkey. The proposed model integrates deep features extracted from lightweight Convolutional Neural Network (CNN) architectures, namely SqueezeNet and MobileNetV2, with textural features obtained through the Gray-Level Co-occurrence Matrix (GLCM). The most significant features were selected using the Chi-square test, and classification was performed with Support Vector Machines (SVM). Experimental results demonstrate that the proposed hybrid model achieved an accuracy of 84.75% in classifying walnut varieties. These findings indicate that the proposed method can provide a reliable, fast, and cost-effective solution for walnut variety identification, with potential benefits for agricultural standardization and precision farming practices.