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

Journal of Imaging

卷:8

Majed Alsanea¹  Noreen Fayyaz Khan²  Sheroz Khan³  Muhammad Islam³  Mohammed F. Alsharekh⁴  Shabana Habib⁵ 

[1] Computing Department, Arabeast Colleges, Riyadh 13544, Saudi Arabia;

[2] Department of Computer Science, Islamia College University, Peshawar 25120, Pakistan;

[3] Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Unayzah 56447, Saudi Arabia;

[4] Department of Electrical Engineering, Unaizah College of Engineering, Qassim University, Unayzah 52571, Saudi Arabia;

[5] Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia;

关键词: red palm weevil;    localization;    classification technique;    deep learning approach;    region convolution neural network;   

DOI  :  10.3390/jimaging8060170

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown