A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization

Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees

An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis Using Deep Ensemble Strategy

Precise and timely diagnosis of Covid-19 and pneumonia is crucial for effective treatment. However, the traditional RT-PCR method is time-consuming, costly, and prone to incorrect results. To address these limitations, a deep ensemble strategy is proposed as a promising alternative to provide more accurate and reliable outcomes. The strategy comprises three main stages: i) pre-processing, ii) salient feature extraction, and iii) training and classification. In the pre-processing step, the authors resize the images to the desired input shape. Data augmentation techniques, such as zooming, nearest full mode, rotation, and flipping, are employed to augment the dataset, thereby improving the training accuracy of the proposed approach. Additionally, the proposed method leverages the capabilities of VGG-16, DenseNet-201, and Efficient-B0 models using transfer-learning techniques to extract deep features from the images. These salient features are then passed through proposed fully connected layers and ensemble classifiers to predict the probability of the given classes. Extensive experiments were conducted on a chest X-ray image dataset, demonstrating that the proposed system outperforms contemporary techniques in terms of precision, recall, F1-score, and accuracy (acc). The proposed method obtained 97% of acc, while 96%, 95%, and 97% pre, rec, and F1-score respectively. In conclusion, this study presents a valuable contribution to medical image diagnosis using an AI-based deep ensemble strategy. The proposed approach offers a promising solution for accurate and efficient diagnosis of Covid-19 and pneumonia, assisting healthcare professionals in making informed decisions for optimal treatment outcomes