{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:04:25Z","timestamp":1774541065349,"version":"3.50.1"},"reference-count":38,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.061995","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T04:23:46Z","timestamp":1747369426000},"page":"1379-1395","source":"Crossref","is-referenced-by-count":5,"title":["Enhanced Wheat Disease Detection Using Deep Learning and Explainable AI Techniques"],"prefix":"10.32604","volume":"84","author":[{"given":"Hussam","family":"Qushtom","sequence":"first","affiliation":[]},{"given":"Ahmad","family":"Hasasneh","sequence":"additional","affiliation":[]},{"given":"Sari","family":"Masri","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","first-page":"385","article-title":"Food security situation of selected highly developed countries against developing countries","volume":"40","author":"Pawlak","year":"2016","journal-title":"J Agribus Rural Dev"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1108\/BFJ-11-2018-0747","article-title":"From precision agriculture to Industry 4.0: unveiling technological connections in the agrifood sector","volume":"121","author":"Trivelli","year":"2019","journal-title":"Br Food J"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"4103","DOI":"10.1109\/TIM.2019.2947125","article-title":"Enabling precision agriculture through embedded sensing with artificial intelligence","volume":"69","author":"Shadrin","year":"2020","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.53022\/oarjms.2024.7.2.0023","article-title":"Adaptive AI in precision agriculture: a review: investigating the use of self-learning algorithms in optimizing farm operations based on real-time data","volume":"7","author":"Akintuyi","year":"2024","journal-title":"Open Access Res J Multidiscip Stud"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1034600","DOI":"10.3389\/fpls.2022.1034600","article-title":"Multi-peril pathogen risks to global wheat production: a probabilistic loss and investment assessment","volume":"13","author":"Chai","year":"2022","journal-title":"Front Plant Sci"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21608\/ejp.2022.117996.1054","article-title":"The impact of wheat yellow rust on quantitative and qualitative grain yield losses under Egyptian field conditions","volume":"50","author":"Mabrouk","year":"2022","journal-title":"Egypt J Phytopathol"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/978-981-19-5443-6_26","author":"Kumari","year":"2023","journal-title":"Sentiment analysis and deep learning"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.procs.2024.04.038","article-title":"EfficientNet architecture and attention mechanism-based wheat disease identification model","volume":"235","author":"Nigam","year":"2024","journal-title":"Procedia Comput Sci"},{"key":"ref9","series-title":"Proceedings of the 2023 3rd International Conference on Intelligent Technologies (CONIT)","article-title":"Automated wheat plant disease detection using deep learning: a multi-class classification approach","author":"Sheenam","year":"2023 Jun 23\u201325"},{"key":"ref10","series-title":"Proceedings of the 2024 IEEE 9th International Conference for Convergence in Technology (I2CT)","article-title":"Machine and deep learning approaches for crop disease detection: an in-depth analysis","author":"Chandu","year":"2024 Apr 5\u20137"},{"key":"ref11","first-page":"110","article-title":"Multifunctional platform and mobile application for plant disease detection","volume":"2507","author":"Uzhinskiy","year":"2019","journal-title":"CEUR Workshop Proc"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/B978-0-323-89778-5.00023-4","author":"Tripathy","year":"2022","journal-title":"Bioinformatics in agriculture"},{"key":"ref13","series-title":"Proceedings of the 2024 International Conference on Inventive Computation Technologies (ICICT)","article-title":"Plant guard: AI-enhanced plant diseases detection for sustainable agriculture","author":"Arulmurugan","year":"2024 Apr 24\u201326"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.gltp.2022.03.016","article-title":"Plant leaf disease detection using computer vision and machine learning algorithms","volume":"3","author":"Harakannanavar","year":"2022","journal-title":"Glob Transit Proc"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.3390\/agriculture12081226","article-title":"Automated wheat diseases classification framework using advanced machine learning technique","volume":"12","author":"Khan","year":"2022","journal-title":"Agriculture"},{"key":"ref16","first-page":"3867","article-title":"A convolutional neural network model for wheat crop disease prediction","volume":"75","author":"Ashraf","year":"2023","journal-title":"Comput Mater Contin"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.3390\/app13095801","article-title":"Lightweight multiscale CNN model for wheat disease detection","volume":"13","author":"Fang","year":"2023","journal-title":"Appl Sci"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"106367","DOI":"10.1016\/j.compag.2021.106367","article-title":"Lightweight convolutional neural network model for field wheat ear disease identification","volume":"189","author":"Bao","year":"2021","journal-title":"Comput Electron Agric"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"100642","DOI":"10.1016\/j.imu.2021.100642","article-title":"Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture","volume":"25","author":"Goyal","year":"2021","journal-title":"Inform Med Unlocked"},{"key":"ref20","first-page":"719","article-title":"Wheat diseases detection and classification using convolutional neural network (CNN)","volume":"13","author":"Hossen","year":"2022","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref21","first-page":"765","article-title":"Crop disease prediction with convolution neural network (CNN) augmented with cellular automata","volume":"19","author":"Pokkuluri","year":"2022","journal-title":"Int Arab J Inf Technol"},{"key":"ref22","first-page":"2125","article-title":"Plant disease diagnosis and image classification using deep learning","volume":"71","author":"Sharma","year":"2021","journal-title":"Comput Mater Contin"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"102068","DOI":"10.1016\/j.ecoinf.2023.102068","article-title":"Deep transfer learning model for disease identification in wheat crop","volume":"75","author":"Nigam","year":"2023","journal-title":"Ecol Inform"},{"key":"ref24","series-title":"Proceedings of the 2021 International Conference on Emerging Smart Computing and Informatics (ESCI)","article-title":"N-CNN based transfer learning method for classification of powdery mildew wheat disease","author":"Kumar","year":"2021 Mar 5\u20137"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.3390\/plants11172230","article-title":"Deep learning utilization in agriculture: detection of rice plant diseases using an improved CNN model","volume":"11","author":"Latif","year":"2022","journal-title":"Plants"},{"key":"ref26","first-page":"100764","article-title":"DeepCrop: deep learning-based crop disease prediction with web application","volume":"14","author":"Islam","year":"2023","journal-title":"J Agric Food Res"},{"key":"ref27","first-page":"197","author":"Sood","year":"2022","journal-title":"Food systems resilience"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"35398","DOI":"10.1109\/ACCESS.2023.3263042","article-title":"FieldPlant: a dataset of field plant images for plant disease detection and classification with deep learning","volume":"11","author":"Moupojou","year":"2023","journal-title":"IEEE Access"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.3390\/rs14143446","article-title":"Evaluation of diverse convolutional neural networks and training strategies for wheat leaf disease identification with field-acquired photographs","volume":"14","author":"Jiang","year":"2022","journal-title":"Remote Sens"},{"key":"ref30","series-title":"Proceedings of the 2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT)","article-title":"Plant leaf disease detection using XAI","author":"Patil","year":"2024 May 3\u20134"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"801","DOI":"10.3390\/e25050801","article-title":"Ensuring explainability and dimensionality reduction in a multidimensional HSI world for early XAI-diagnostics of plant stress","volume":"25","author":"Lysov","year":"2023","journal-title":"Entropy"},{"key":"ref32","unstructured":"Lundberg S, Lee SI. A unified approach to interpreting model predictions. arXiv:1705.07874v2. 2017."},{"key":"ref33","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","article-title":"Why should I trust you?: explaining the predictions of any classifier","author":"Ribeiro","year":"2016 Aug 13\u201317"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"7903","DOI":"10.3390\/s21237903","article-title":"Super resolution generative adversarial network (SRGANS) for wheat stripe rust classification","volume":"21","author":"Maqsood","year":"2021","journal-title":"Sensors"},{"key":"ref35","first-page":"1806","article-title":"An artificial intelligence based weed classification using VGG16 classifier and rmsprop optimizer","volume":"100","author":"Dnvsls","year":"2022","journal-title":"J Theor Appl Inf Technol"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"105712","DOI":"10.1016\/j.compag.2020.105712","article-title":"Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset","volume":"177","author":"Xiong","year":"2020","journal-title":"Comput Electron Agric"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2017.01852","article-title":"Deep learning for image-based cassava disease detection","volume":"8","author":"Ramcharan","year":"2017","journal-title":"Front Plant Sci"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"751","DOI":"10.3390\/info15120751","article-title":"Exploring the impact of image-based audio representations in classification tasks using vision transformers and explainable AI techniques","volume":"15","author":"Sari","year":"2024","journal-title":"Information"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_61995\/TSP_CMC_61995.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:34:57Z","timestamp":1763343297000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61712"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.061995","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}