Articles

Tomato farming plays a major role in Ghana’s agricultural sector by contributing to food supply and serving as a source of income for many smallholder farmers. However, tomato plants are easily affected by leaf diseases that can spread quickly and reduce crop yield if not detected early. In many farming communities, disease detection is still done manually, which is often slow, inconsistent, and affected by human error. This study developed a deep learning-based system for the early detection of tomato leaf diseases using a Convolutional Neural Network (CNN) based on the VGG16 architecture. A total of 16,211 tomato leaf images, comprising healthy leaves and nine disease classes, were used for the study. The images were resized, normalized, and enhanced through data augmentation techniques such as rotation, flipping, shifting, and zooming. The model achieved its highest validation accuracy of 96.8% at epoch 16, while the final validation accuracy at epoch 20 was 94.30%, demonstrating strong performance in tomato disease classification.

The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.