convolutional neural networks

A data-driven fusion of deep learning and transfer learning for orange disease classification

In agriculture, early detection of crop diseases is imperative for sustainability and maximizing yields.  Rooted in Agriculture 4.0, our innovative approach  combines pre-trained Convolutional Neural Networks (CNNs) models with data-driven solutions to address global challenges related to water scarcity.  By integrating the combined $L_{1}/L_{2}$ regularization technique to our model layers, we enhance their flexibility, reducing the risk of the overfitting effect of the model.  In the orange dataset used in our experiments, we have 1790 orange images, including a class

RESEARCH ON THE STATE-OF-THE-ART DEEP LEARNING BASED MODELS FOR FACE DETECTION AND RECOGNITION

The problem of building a face recognition pipeline faces numerous challenges such as changes in lighting, pose, and facial expressions. The main stages of the pipeline include detection, alignment, feature extraction, and face representation. Each of these stages is critically important for achieving accurate recognition.

USING NEURAL NETWORKS TO IDENTIFY OBJECTS IN AN IMAGE

A modified neural network model based on Yolo V5 was developed and the quality metrics of object classification on video images built on the basis of existing known basic neural network architectures were compared. The application of convolutional neural networks for processing images from video surveillance cameras is considered in order to develop an optimized algorithm for detecting and classifying objects on video images. The existing models and architectures of neural networks for image analysis were analyzed and compared.

RESEARCH OF PLANT DISEASE DIAGNOSTIC METHODS USING DEEP LEARNING

The article explores the use of convolutional neural networks (CNNs) in the diagnosis and identification of plant diseases and pests. Various methods of plant disease diagnosis, features of datasets, and challenges in this research direction are considered. The article discusses a five-step methodology for determining plant diseases, including data collection, preprocessing, segmentation, feature extraction, and classification. Different deep learning architectures enabling fast and efficient plant disease diagnosis are investigated.