{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T03:23:02Z","timestamp":1770520982679,"version":"3.49.0"},"reference-count":23,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T00:00:00Z","timestamp":1655251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2014the 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.<\/jats:p>","DOI":"10.3390\/jimaging8060170","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T23:50:14Z","timestamp":1655250614000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2381-1223","authenticated-orcid":false,"given":"Majed","family":"Alsanea","sequence":"first","affiliation":[{"name":"Computing Department, Arabeast Colleges, Riyadh 13544, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-2520","authenticated-orcid":false,"given":"Shabana","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noreen Fayyaz","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Islamia College University, Peshawar 25120, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9021-180X","authenticated-orcid":false,"given":"Mohammed F.","family":"Alsharekh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Unaizah College of Engineering, Qassim University, Unayzah 52571, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-4451","authenticated-orcid":false,"given":"Muhammad","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Unayzah 56447, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5749-8538","authenticated-orcid":false,"given":"Sheroz","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Unayzah 56447, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.37624\/IJERT\/13.8.2020.1917-1920","article-title":"Date Fruits Grading and Sorting Classification Algorithm Using Colors and Shape Features","volume":"13","author":"Alturki","year":"2020","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lima, M.C.F., de Almeida Leandro, M.E.D., Valero, C., Coronel, L.C.P., and Bazzo, C.O.G. (2020). Automatic detection and monitoring of insect pests\u2014A review. Agriculture, 10.","DOI":"10.3390\/agriculture10050161"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3390\/insects12070594","article-title":"The Effect of Gut Bacteria on the Physiology of Red Palm Weevil, Rhynchophorus ferrugineus Olivier and Their Potential for the Control of This Pest","volume":"12","author":"Bing","year":"2021","journal-title":"Insects"},{"key":"ref_4","first-page":"e20207701","article-title":"Artificial intelligence applications in the agriculture 4.0","volume":"51","author":"Megeto","year":"2021","journal-title":"Rev. Ci\u00eanc. Agron."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, W., Xing, K., Qiao, J., Wang, X., Gao, L., and Shen, Z. (2010, January 10\u201312). Research on insect identification based on pattern recognition technology. Proceedings of the 2010 Sixth International Conference on Natural Computation, Yantai, China.","DOI":"10.1109\/ICNC.2010.5583156"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105482","DOI":"10.1016\/j.dib.2020.105482","article-title":"Dataset on the influence of relative humidity on the pathogenicity of Metarhizium anisopliae isolates from Thailand and Malaysia against red palm weevil (Rhynchophorus ferrugineus, Olivier) adult","volume":"30","author":"Cheong","year":"2020","journal-title":"Data Brief"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"356","DOI":"10.3844\/ajabssp.2011.356.364","article-title":"Artificial neural networks based red palm weevil (Rynchophorus ferrugineous, Olivier) recognition system","volume":"6","author":"Hassan","year":"2011","journal-title":"Am. J. Agric. Biol. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3844\/ajabssp.2012.36.42","article-title":"Support vector machine based red palm weevil (Rynchophorus ferrugineous, Olivier) recognition system","volume":"7","author":"Hassan","year":"2012","journal-title":"Am. J. Agric. Biol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1653\/024.094.0405","article-title":"Recent developments in the use of acoustic sensors and signal processing tools to target early infestations of red palm weevil in agricultural environments","volume":"94","author":"Mankin","year":"2011","journal-title":"Fla. Entomol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.3390\/s130201706","article-title":"On the Design of a Bioacoustic Sensor for the Early Detection of the Red Palm Weevil","volume":"13","author":"Rach","year":"2013","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mohammed, M., El-Shafie, H., and Alqahtani, N. (2021). Design and Validation of Computerized Flight-Testing Systems with Controlled Atmosphere for Studying Flight Behavior of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier). Sensors, 21.","DOI":"10.3390\/s21062112"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105836","DOI":"10.1016\/j.compag.2020.105836","article-title":"Detection and classification of soybean pests using deep learning with UAV images","volume":"179","author":"Tetila","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Adedeji, A.A., Ekramirad, N., Rady, A., Hamidisepehr, A., Donohue, K.D., Villanueva, R.T., Parrish, C.A., and Li, M. (2020). Non-destructive technologies for detecting insect infestation in fruits and vegetables under postharvest conditions: A critical review. Foods, 9.","DOI":"10.3390\/foods9070927"},{"key":"ref_14","first-page":"1137","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"9","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3276704","DOI":"10.1155\/2022\/3276704","article-title":"A Real-Time Framework for Human Face Detection and Recognition in CCTV Images","volume":"2022","author":"Ullah","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gao, J., Yang, Z., and Nevatia, R. (2017). Cascaded boundary regression for temporal action detection. arXiv.","DOI":"10.5244\/C.31.52"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, H., Das, A., and Saenko, K. (2017, January 22\u201329). R-c3d: Region convolutional 3d network for temporal activity detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.617"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/smartcities4020024","article-title":"IoT in Smart Cities: A Survey of Technologies","volume":"4","author":"Syed","year":"2021","journal-title":"Pract. Chall. Smart Cities"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kurdi, H., Al-Aldawsari, A., Al-Turaiki, I., and Aldawood, A.S. (2021). Early detection of red palm weevil, Rhynchophorus ferrugineus (Olivier), infestation using data mining. Plants, 10.","DOI":"10.3390\/plants10010095"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alyahya, S., Khan, W.U., Ahmed, S., Marwat, S.N.K., and Habib, S. (2022). Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices. Electronics, 11.","DOI":"10.3390\/electronics11060963"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kuznetsova, A., Maleva, T., and Soloviev, V. (2020). Using YOLOv3 algorithm with pre-and post-processing for apple detection in fruit-harvesting robot. Agronomy, 10.","DOI":"10.3390\/agronomy10071016"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Harith-Fadzilah, N., Lam, S.D., Haris-Hussain, M., Ghani, I.A., Zainal, Z., Jalinas, J., and Hassan, M. (2021). Proteomics and Interspecies Interaction Analysis Revealed Abscisic Acid Signalling to Be the Primary Driver for Oil Palm\u2019s Response against Red Palm Weevil Infestation. Plants, 10.","DOI":"10.3390\/plants10122574"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"365","DOI":"10.3390\/s22072678","article-title":"Vision-Based Learning from Demonstration System for Robot Arms","volume":"22","author":"Hwang","year":"2022","journal-title":"Sensors"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/6\/170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:31:40Z","timestamp":1760139100000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/6\/170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,15]]},"references-count":23,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["jimaging8060170"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8060170","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,15]]}}}