![]() ![]() 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. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes-the selection target. 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. Researchers had not applied deep learning to the classification of red palm weevils previously. 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. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. 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. 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. Problem: The RPW has caused considerable damage to various palm species. ![]() 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. ![]()
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