{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:52:08Z","timestamp":1778615528575,"version":"3.51.4"},"reference-count":30,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2025R904"],"award-info":[{"award-number":["PNURSP2025R904"]}]},{"name":"Princess Nourah bint Abdulrahman University, Riyadh,Saudi Arabia"},{"DOI":"10.13039\/100019779","name":"Qatar National Library","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019779","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3632545","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:45:07Z","timestamp":1763059507000},"page":"202214-202227","source":"Crossref","is-referenced-by-count":1,"title":["Drone-Aided Plants Health Monitoring Using Enhanced Vision Transformer"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7211-4646","authenticated-orcid":false,"given":"Junaid Ahmad","family":"Khan","sequence":"first","affiliation":[{"name":"Seoul National University of Science and Technology, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2925-8841","authenticated-orcid":false,"given":"Muhammad Asif","family":"Khan","sequence":"additional","affiliation":[{"name":"Qatar Mobility Innovations Center, Qatar University, Doha, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imen","family":"Filali","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3324722"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2022.3195291"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3069646"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3286730"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.13052\/jmm1550-4646.18314"},{"key":"ref6","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1mb model size","author":"Iandola","year":"2016","journal-title":"arXiv:1602.07360"},{"key":"ref7","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref8","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019","journal-title":"arXiv:1905.11946"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-023-01286-8"},{"key":"ref10","first-page":"455","article-title":"DroneNet: Crowd density estimation using self-ONNs for drones","volume-title":"Proc. IEEE 20th Consum. Commun. Netw. Conf. (CCNC)","author":"Khan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TAFE.2024.3445119"},{"key":"ref12","article-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"issue":"1","key":"ref13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3447085"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2016.014192016"},{"key":"ref16","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. 25th Int. Conf. Neural Inf. Process. Syst.","volume":"1","author":"Krizhevsky"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10152-y"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ohx.2022.e00363"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105933"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.32604\/iasc.2022.017706"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.34133\/2019\/9237136","article-title":"How convolutional neural networks diagnose plant disease","volume":"2019","author":"Toda","year":"2019","journal-title":"Plant Phenomics"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3358333"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCI54379.2022.9740975"},{"issue":"1","key":"ref26","doi-asserted-by":"crossref","first-page":"55","DOI":"10.13164\/mendel.2022.1.055","article-title":"Color-aware two-branch DCNN for efficient plant disease classification","volume":"28","author":"Schwarz Schuler","year":"2022","journal-title":"MENDEL"},{"key":"ref27","first-page":"375","article-title":"Reliable deep learning plant leaf disease classification based on light-chroma separated branches","volume-title":"Proc. Int. Conf. Catalan Assoc. Artif. Intell.","author":"Schuler"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105162","article-title":"Deep learning for classification and severity estimation of coffee leaf biotic stress","volume":"169","author":"Esgario","year":"2020","journal-title":"Comput. Electron. Agricult."},{"key":"ref29","article-title":"An open access repository of images on plant health to enable the development of mobile disease diagnostics","author":"Hughes","year":"2015","journal-title":"arXiv:1511.08060"},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2023.109306","article-title":"CCMT: Dataset for crop pest and disease detection","volume":"49","author":"Mensah","year":"2023","journal-title":"Data Brief"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11244794.pdf?arnumber=11244794","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:40:01Z","timestamp":1764960001000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11244794\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3632545","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}