{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:21:30Z","timestamp":1779099690613,"version":"3.51.4"},"reference-count":115,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100019725","name":"Deanship of Scientific Research, Prince Sattam bin Abdulaziz University","doi-asserted-by":"publisher","award":["No. 01\/2024\/28439."],"award-info":[{"award-number":["No. 01\/2024\/28439."]}],"id":[{"id":"10.13039\/100019725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s44443-025-00040-3","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T10:14:58Z","timestamp":1746785698000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research"],"prefix":"10.1007","volume":"37","author":[{"given":"Sajid Ullah","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anas","family":"Alsuhaibani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulrahman","family":"Alabduljabbar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahdah","family":"Almarshad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youssef N.","family":"Altherwy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tallha","family":"Akram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.3390\/agronomy13061524","volume":"13","author":"A Abbas","year":"2023","unstructured":"Abbas A, Zhang Z, Zheng H, Alami MM, Alrefaei AF, Abbas Q, Naqvi SAH, Rao MJ, Mosa WF, Abbas Q (2023) Drones in plant disease assessment, efficient monitoring, and detection: a way forward to smart agriculture. Agronomy 13:1524","journal-title":"Agronomy"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Abdelkrim O (2024) Hyperspectral imaging using deep learning in wheat diseases. In: 2024 8th international conference on image and signal processing and their applications (ISPA). IEEE, pp 1\u20138","DOI":"10.1109\/ISPA59904.2024.10536831"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Abdullah HM, Mohana NT, Khan BM, Ahmed SM, Hossain M, Islam KS,..., Ahamed T (2023) Present and future scopes and challenges of plant pest and disease (P&D) monitoring: Remote sensing, image processing, and artificial intelligence perspectives.\u00a0Remote Sens Appl: Soc Environ 32:100996","DOI":"10.1016\/j.rsase.2023.100996"},{"key":"40_CR4","doi-asserted-by":"crossref","first-page":"55","DOI":"10.11591\/csit.v5i1.pp55-62","volume":"5","author":"TS Adekunle","year":"2024","unstructured":"Adekunle TS, Lawrence MO, Alabi OO, Afolorunso AA, Ebong GN, Oladipupo MA (2024) Deep learning technique for plant disease detection. Comput Sci Inf Technol 5:55\u201362","journal-title":"Comput Sci Inf Technol"},{"key":"40_CR5","doi-asserted-by":"crossref","first-page":"9042","DOI":"10.1109\/ACCESS.2023.3240100","volume":"11","author":"A Ahmad","year":"2023","unstructured":"Ahmad A, El Gamal A, Saraswat D (2023a) Toward generalization of deep learning-based plant disease identification under controlled and field conditions. IEEE Access 11:9042\u20139057","journal-title":"IEEE Access"},{"key":"40_CR6","doi-asserted-by":"crossref","first-page":"100083","DOI":"10.1016\/j.atech.2022.100083","volume":"3","author":"A Ahmad","year":"2023","unstructured":"Ahmad A, Saraswat D, El Gamal A (2023b) A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric Technol 3:100083","journal-title":"Smart Agric Technol"},{"key":"40_CR7","unstructured":"Ali Z (2024) Yellow rust disease detection from wheat images. https:\/\/urn.fi\/URN:NBN:fi:amk-2024052715871"},{"issue":"1","key":"40_CR8","first-page":"1","volume":"15","author":"T Angamuthu","year":"2023","unstructured":"Angamuthu T, Arunachalam AS (2023) A comprehensive survey on the revolution of plant disease detection and diagnosis through automated image processing techniques with CNN and RNN. Singaporean J Sci Res (SJSR) 15(1):1\u20138","journal-title":"Singaporean J Sci Res (SJSR)"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Anju UV, Swaraj KP (2024) Real time iot enabled automated leaf disease identification using deep learning models\u2013a review. In: AIP conference proceedings, vol 3037. AIP Publishing","DOI":"10.1063\/5.0196091"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Banerjee D, Kukreja V, Chandran N, Garg N (2024) Precision diagnosis of wheat powdery mildew using CNN and random forest. In: 2024 international conference on emerging smart computing and informatics (ESCI). IEEE, pp 1\u20136","DOI":"10.1109\/ESCI59607.2024.10497264"},{"key":"40_CR11","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TLA.2018.8444395","volume":"16","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA, Koenigkan LV, Halfeld-Vieira BA, Costa RV, Nechet KL, Godoy CV, Junior ML, Patricio FRA, Talamini V, Chitarra LG (2018) Annotated plant pathology databases for image-based detection and recognition of diseases. IEEE Lat Am Trans 16:1749\u20131757","journal-title":"IEEE Lat Am Trans"},{"key":"40_CR12","doi-asserted-by":"crossref","first-page":"327","DOI":"10.3390\/agronomy14020327","volume":"14","author":"U Barman","year":"2024","unstructured":"Barman U, Sarma P, Rahman M, Deka V, Lahkar S, Sharma V, Saikia MJ (2024) Vit-SmartAgri: vision transformer and smartphone-based plant disease detection for smart agriculture. Agronomy 14:327","journal-title":"Agronomy"},{"issue":"8","key":"40_CR13","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.1007\/s11042-024-18733-8","volume":"84","author":"A Bhola","year":"2025","unstructured":"Bhola A, Kumar P (2025) Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat. Multimed Tools Appl 84(8):4751\u20134771","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"40_CR14","first-page":"54","volume":"12","author":"I Bouacida","year":"2025","unstructured":"Bouacida I, Farou B, Djakhdjakha L, Seridi H, Kurulay M (2025) Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves. Inf Process Agric 12(1):54\u201367","journal-title":"Inf Process Agric"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Cavender-Bares J, Gamon JA, Townsend PA (2020) Remote sensing of plant biodiversity. Springer Nature, p 581","DOI":"10.1007\/978-3-030-33157-3"},{"key":"40_CR16","first-page":"175","volume":"12","author":"K Chelladurai","year":"2024","unstructured":"Chelladurai K, Sujatha N (2024) A review of disease detection in leaves using image processing techniques based on thermal camera. Int J Intell Syst Appl Eng 12:175\u2013184","journal-title":"Int J Intell Syst Appl Eng"},{"key":"40_CR17","doi-asserted-by":"crossref","first-page":"12359","DOI":"10.1007\/s12652-022-04334-6","volume":"14","author":"J Chen","year":"2023","unstructured":"Chen J, Zeb A, Nanehkaran YA, Zhang D (2023) Stacking ensemble model of deep learning for plant disease recognition. J Ambient Intell Humaniz Comput 14:12359\u201312372","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"40_CR18","volume":"179","author":"X Chen","year":"2024","unstructured":"Chen X, Liu T, Han K, Jin X, Yu J (2024) Semi-supervised learning for detection of sedges in sod farms. Crop Prot 179:106626","journal-title":"Crop Prot"},{"key":"40_CR19","unstructured":"Ciobotari I, Pr\u00edncipe A, Oliveira MA, Silva JN (2024) LiDAR data acquisition and processing for ecology applications. arXiv preprint arXiv:2401.05891"},{"key":"40_CR20","doi-asserted-by":"crossref","first-page":"1392409","DOI":"10.3389\/fpls.2024.1392409","volume":"15","author":"C Cuenca-Romero","year":"2024","unstructured":"Cuenca-Romero C, Apolo-Apolo OE, Rodr\u00edguez V\u00e1zquez JN, Egea G, Perez-Ruiz M (2024) Tackling unbalanced datasets for yellow and brown rust detection in wheat. Front Plant Sci 15:1392409","journal-title":"Front Plant Sci"},{"key":"40_CR21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40537-023-00863-9","volume":"11","author":"WB Demilie","year":"2024","unstructured":"Demilie WB (2024) Plant disease detection and classification techniques: a comparative study of the performances. J Big Data 11:5","journal-title":"J Big Data"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Dey P, Mahmud T, Nahar SR, Hossain MS, Andersson K (2024) Plant disease detection in precision agriculture: deep learning approaches. In: 2024 2nd international conference on intelligent data communication technologies and internet of things (IDCIoT). IEEE, pp 661\u2013667","DOI":"10.1109\/IDCIoT59759.2024.10467525"},{"key":"40_CR23","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s00217-023-04386-8","volume":"250","author":"A Diker","year":"2024","unstructured":"Diker A, Elen A, K\u00f6zkurt C, K\u0131l\u0131\u00e7arslan S, D\u00f6nmez E, Arslan K, Kuran EC (2024) An effective feature extraction method for olive peacock eye leaf disease classification. Eur Food Res Technol 250:287\u2013299","journal-title":"Eur Food Res Technol"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Dixit N, Arora R, Gupta D (2024) Wheat crop disease detection and classification using machine learning. In: Infrastructure possibilities and human-centered approaches with industry 5.0. IGI Global Scientific Publishing, pp 267\u2013280","DOI":"10.4018\/979-8-3693-0782-3.ch016"},{"key":"40_CR25","first-page":"87","volume":"14","author":"AK Dohare","year":"2024","unstructured":"Dohare AK, Khan AA (2024) Plant health monitoring system using machine learning. Int J Eng Manag Res 14:87\u201394","journal-title":"Int J Eng Manag Res"},{"key":"40_CR26","doi-asserted-by":"crossref","first-page":"25543","DOI":"10.1007\/s11042-023-16475-7","volume":"83","author":"RK Dubey","year":"2024","unstructured":"Dubey RK, Choubey DK (2024) Adaptive feature selection with deep learning MBi-LSTM model based paddy plant leaf disease classification. Multimed Tools Appl 83:25543\u201325571","journal-title":"Multimed Tools Appl"},{"key":"40_CR27","doi-asserted-by":"crossref","first-page":"0163","DOI":"10.34133\/plantphenomics.0163","volume":"6","author":"J Feng","year":"2024","unstructured":"Feng J, Zhang S, Zhai Z, Yu H, Xu H (2024) Dc2net: an asian soybean rust detection model based on hyperspectral imaging and deep learning. Plant Phenomics 6:0163","journal-title":"Plant Phenomics"},{"key":"40_CR28","doi-asserted-by":"crossref","first-page":"10989","DOI":"10.1007\/s11042-023-16012-6","volume":"83","author":"V Gautam","year":"2024","unstructured":"Gautam V, Ranjan RK, Dahiya P, Kumar A (2024) ESDNN: a novel ensembled stack deep neural network for mango leaf disease classification and detection. Multimed Tools Appl 83:10989\u201311015","journal-title":"Multimed Tools Appl"},{"key":"40_CR29","first-page":"307","volume":"39","author":"N Gogoi","year":"2018","unstructured":"Gogoi N, Deka B, Bora L (2018) Remote sensing and its use in detection and monitoring plant diseases: a review. Agric Rev 39:307\u2013313","journal-title":"Agric Rev"},{"key":"40_CR30","first-page":"103834","volume":"129","author":"D Han","year":"2024","unstructured":"Han D, Wang P, Tansey K, Zhang Y, Li H (2024) A graph-based deep learning framework for field scale wheat yield estimation. Int J Appl Earth Obs Geoinf 129:103834","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"40_CR31","doi-asserted-by":"crossref","first-page":"e0297983","DOI":"10.1371\/journal.pone.0297983","volume":"19","author":"SCA Houetohossou","year":"2024","unstructured":"Houetohossou SCA, Ratheil Houndji V, Sikirou R, Gl\u00e8l\u00e8 Kaka\u00ef R (2024) Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth algorithm. Plos One 19:e0297983","journal-title":"Plos One"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"HR SK, KM P (2024) Deep convolutional neural networks fusion with support vector machines and K-nearest neighbors for precise crop leaf disease classification.\u00a0IJACSA 15(3)","DOI":"10.14569\/IJACSA.2024.0150356"},{"key":"40_CR33","doi-asserted-by":"crossref","first-page":"100441","DOI":"10.1016\/j.measen.2022.100441","volume":"24","author":"C Jackulin","year":"2022","unstructured":"Jackulin C, Murugavalli S (2022) A comprehensive review on detection of plant disease using machine learning and deep learning approaches. Meas Sensors 24:100441","journal-title":"Meas Sensors"},{"key":"40_CR34","doi-asserted-by":"crossref","first-page":"1356260","DOI":"10.3389\/fpls.2024.1356260","volume":"15","author":"A Jafar","year":"2024","unstructured":"Jafar A, Bibi N, Naqvi RA, Sadeghi-Niaraki A, Jeong D (2024) Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Front Plant Sci 15:1356260","journal-title":"Front Plant Sci"},{"key":"40_CR35","doi-asserted-by":"crossref","unstructured":"Jagadeeshan N, Nagendar Y, Swarnalatha K, Al-Attabi K, Ramachandra AC (2024) Three-dimensional convolutional neural network for wheat rust disease classification. In: 2024 international conference on distributed computing and optimization techniques (ICDCOT). IEEE, pp 1\u20134","DOI":"10.1109\/ICDCOT61034.2024.10515754"},{"key":"40_CR36","doi-asserted-by":"crossref","first-page":"16310","DOI":"10.1109\/ACCESS.2024.3358333","volume":"12","author":"DS Joseph","year":"2024","unstructured":"Joseph DS, Pawar PM, Chakradeo K (2024) Real-time plant disease dataset development and detection of plant disease using deep learning. IEEE Access 12:16310\u201316333","journal-title":"IEEE Access"},{"key":"40_CR37","doi-asserted-by":"crossref","first-page":"561","DOI":"10.3390\/agriculture13030561","volume":"13","author":"H Jung","year":"2023","unstructured":"Jung H, Tajima R, Ye R, Hashimoto N, Yang Y, Yamamoto S, Homma K (2023) Utilization of UAV remote sensing in plant-based field experiments: a case study of the evaluation of LAI in a small-scale sweetcorn experiment. Agriculture 13:561","journal-title":"Agriculture"},{"key":"40_CR38","doi-asserted-by":"crossref","first-page":"145","DOI":"10.3390\/fi16050145","volume":"16","author":"Z Kamarianakis","year":"2024","unstructured":"Kamarianakis Z, Perdikakis S, Daliakopoulos IN, Papadimitriou DM, Panagiotakis S (2024) Design and Implementation of a low-cost, linear robotic camera system, targeting greenhouse plant growth monitoring. Futur Internet 16:145","journal-title":"Futur Internet"},{"key":"40_CR39","doi-asserted-by":"crossref","first-page":"60","DOI":"10.23851\/mjs.v35i1.1416","volume":"35","author":"MR Kareem","year":"2024","unstructured":"Kareem MR (2024) Image analysis and detection of olive leaf diseases using recurrent neural networks. Al-Mustansiriyah J Sci 35:60\u201365","journal-title":"Al-Mustansiriyah J Sci"},{"issue":"6","key":"40_CR40","first-page":"32","volume":"18","author":"R Karthickmanoj","year":"2023","unstructured":"Karthickmanoj R, Sasilatha T, Singh NSS (2023) Plant disease detection using random forest classifier with novel segmentation and feature extraction strategy. J Eng Sci Technol 18(6):32\u201338","journal-title":"J Eng Sci Technol"},{"key":"40_CR41","doi-asserted-by":"crossref","first-page":"75","DOI":"10.59543\/ijmscs.v2i.8343","volume":"2","author":"MM Khalid","year":"2024","unstructured":"Khalid MM, Karan O (2024) Deep learning for plant disease detection. Int J Math Stat Comput Sci 2:75\u201384","journal-title":"Int J Math Stat Comput Sci"},{"key":"40_CR42","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.3390\/agriculture12081226","volume":"12","author":"H Khan","year":"2022","unstructured":"Khan H, Haq IU, Munsif M, Mustaqeem, Khan SU, Lee MY (2022) Automated wheat diseases classification framework using advanced machine learning technique. Agriculture 12:1226","journal-title":"Agriculture"},{"issue":"29","key":"40_CR43","doi-asserted-by":"crossref","first-page":"72221","DOI":"10.1007\/s11042-024-18463-x","volume":"83","author":"D Kumar","year":"2024","unstructured":"Kumar D, Kukreja V, Singh A (2024a) A novel hybrid segmentation technique for identification of wheat rust diseases. Multimed Tools Appl 83(29):72221\u201372251","journal-title":"Multimed Tools Appl"},{"key":"40_CR44","doi-asserted-by":"crossref","unstructured":"Kumar V, Banerjee D, Chauhan R, Thapliyal S, Sharma K, Gill KS (2024b) Unified methodology for wheat leaf disease identification: combining CNN and random forest approaches. In: 2024 3rd international conference for innovation in technology (INOCON). IEEE, pp 1\u20135","DOI":"10.1109\/INOCON60754.2024.10511519"},{"key":"40_CR45","doi-asserted-by":"crossref","unstructured":"Kumar V, Gill KS, Chauhan R, Thapliyal S, Sharma K (2024c) Enhancing wheat disease classification: a hybrid approach combining CNN and random forest. In 2024 fourth international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT). IEEE, pp 1\u20137","DOI":"10.1109\/ICAECT60202.2024.10469310"},{"key":"40_CR46","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.3390\/agronomy12122933","volume":"12","author":"Y Li","year":"2022","unstructured":"Li Y, Qiao T, Leng W, Jiao W, Luo J, Lv Y, Tong Y, Mei X, Li H, Hu Q (2022) Semantic segmentation of wheat stripe rust images using deep learning. Agronomy 12:2933","journal-title":"Agronomy"},{"key":"40_CR47","doi-asserted-by":"crossref","first-page":"109029","DOI":"10.1016\/j.compag.2024.109029","volume":"222","author":"H Liu","year":"2024","unstructured":"Liu H, Chen H, Du J, Xie C, Zhou Q, Wang R, Jiao L (2024) Auto-adjustment label assignment-based convolutional neural network for oriented wheat diseases detection. Comput Electron Agric 222:109029","journal-title":"Comput Electron Agric"},{"key":"40_CR48","doi-asserted-by":"crossref","first-page":"116196","DOI":"10.1109\/ACCESS.2023.3325747","volume":"11","author":"D Lu","year":"2023","unstructured":"Lu D, Ye J, Wang Y, Yu Z (2023) Plant detection and counting: enhancing precision agriculture in UAV and general scenes. IEEE Access 11:116196\u2013116205","journal-title":"IEEE Access"},{"key":"40_CR49","doi-asserted-by":"crossref","first-page":"e2024001","DOI":"10.1590\/s1982-21702024000100001","volume":"30","author":"JC Macu\u00e1cua","year":"2024","unstructured":"Macu\u00e1cua JC, Centeno JAS, Amisse C, Jij\u00f3n-Palma ME, Vestena KDM (2024) Automatic foliar spot detection from low-cost RGB digital images using a hybrid approach of convolutional neural network and random forest classifier. Boletim Ci\u00ean Geod\u00e9sicas 30:e2024001","journal-title":"Boletim Ci\u00ean Geod\u00e9sicas"},{"key":"40_CR50","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.procs.2024.06.049","volume":"238","author":"M Madeira","year":"2024","unstructured":"Madeira M, Porf\u00edrio RP, Santos PA, Madeira RN (2024) AI-powered solution for plant disease detection in viticulture. Procedia Comput Sci 238:468\u2013475","journal-title":"Procedia Comput Sci"},{"key":"40_CR51","doi-asserted-by":"crossref","first-page":"100534","DOI":"10.1016\/j.prime.2024.100534","volume":"8","author":"K Mahadevan","year":"2024","unstructured":"Mahadevan K, Punitha A, Suresh J (2024) Automatic recognition of rice plant leaf diseases detection using deep neural network with improved threshold neural network. e-Prime-Adv Electr Eng Electron Energy 8:100534","journal-title":"e-Prime-Adv Electr Eng Electron Energy"},{"key":"40_CR52","doi-asserted-by":"crossref","unstructured":"Mandava M, Vinta SR, Ghosh H, Rahat IS (2024) Identification and categorization of yellow rust infection in wheat through deep learning techniques.\u00a0EAI Endorsed Trans Internet Things\u00a010","DOI":"10.4108\/eetiot.5325"},{"key":"40_CR53","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s11119-023-10093-x","volume":"25","author":"R Mao","year":"2024","unstructured":"Mao R, Zhang Y, Wang Z, Hao X, Zhu T, Gao S, Hu X (2024) DAE-Mask: a novel deep-learning-based automatic detection model for in-field wheat diseases. Precis Agric 25:785\u2013810","journal-title":"Precis Agric"},{"key":"40_CR54","doi-asserted-by":"crossref","unstructured":"Maqsood Y, Usman SM, Alhussein M, Aurangzeb K, Khalid S, Zubair M (2024) Model agnostic meta-learning (MAML)-based ensemble model for accurate detection of wheat diseases using vision transformer and graph neural networks. Computers, Materials & Continua 79(2)","DOI":"10.32604\/cmc.2024.049410"},{"key":"40_CR55","doi-asserted-by":"crossref","first-page":"1373881","DOI":"10.3389\/fenve.2024.1373881","volume":"3","author":"M Mng\u2019ombe","year":"2024","unstructured":"Mng\u2019ombe M, Mtonga E, Chunga B, Chidya R, Malota M (2024) Comparative study for the performance of pure artificial intelligence software sensor and self-organizing map assisted software sensor in predicting 5-day biochemical oxygen demand for Kauma Sewage Treatment Plant effluent in Malawi. Front Environ Eng 3:1373881","journal-title":"Front Environ Eng"},{"key":"40_CR56","doi-asserted-by":"crossref","unstructured":"Mohammad A, Eleyan D, Eleyan A, Bejaoui T (2024) IoT-based plant disease detection using machine learning: a systematic literature review. In: 2024 international conference on smart applications, communications and networking (SmartNets). IEEE,\u00a0pp 1-7","DOI":"10.1109\/SmartNets61466.2024.10577751"},{"key":"40_CR57","doi-asserted-by":"crossref","unstructured":"Mohapatra SK, Prasad S, Nayak SC (2021) Wheat rust disease detection using deep learning. In: Data science and data analytics: opportunities and challenges. p 191","DOI":"10.1201\/9781003111290-11-14"},{"key":"40_CR58","doi-asserted-by":"crossref","first-page":"155","DOI":"10.3390\/agriengineering6010010","volume":"6","author":"D Mojaravscki","year":"2024","unstructured":"Mojaravscki D, Graziano Magalh\u00e3es PS (2024) Comparative evaluation of color correction as image preprocessing for olive identification under natural light using cell phones. AgriEngineering 6:155\u2013170","journal-title":"AgriEngineering"},{"key":"40_CR59","doi-asserted-by":"crossref","unstructured":"Morchid A, Marhoun M, El Alami R, Boukili B (2024) Intelligent detection for sustainable agriculture: A review of IoT-based embedded systems, cloud platforms, DL, and ML for plant disease detection. Multimed Tools Appl 1\u201340","DOI":"10.1007\/s11042-024-18392-9"},{"key":"40_CR60","doi-asserted-by":"crossref","unstructured":"Najafabadi MA, Kazemi I (2024) Systemic design of the very-high-resolution imaging payload of an optical remote sensing satellite for launch into the VLEO using an small launch vehicle. Heliyon 10(6)","DOI":"10.1016\/j.heliyon.2024.e27404"},{"issue":"10","key":"40_CR61","doi-asserted-by":"crossref","first-page":"5982","DOI":"10.3390\/app13105982","volume":"13","author":"MS Ngongoma","year":"2023","unstructured":"Ngongoma MS, Kabeya M, Moloi K (2023) A review of plant disease detection systems for farming applications. Appl Sci 13(10):5982","journal-title":"Appl Sci"},{"key":"40_CR62","doi-asserted-by":"crossref","first-page":"102068","DOI":"10.1016\/j.ecoinf.2023.102068","volume":"75","author":"S Nigam","year":"2023","unstructured":"Nigam S, Jain R, Marwaha S, Arora A, Haque MA, Dheeraj A, Singh VK (2023) Deep transfer learning model for disease identification in wheat crop. Eco Inform 75:102068","journal-title":"Eco Inform"},{"key":"40_CR63","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.procs.2024.04.038","volume":"235","author":"S Nigam","year":"2024","unstructured":"Nigam S, Jain R, Singh VK, Marwaha S, Arora A, Jain S (2024) EfficientNet architecture and attention mechanism-based wheat disease identification model. Procedia Comput Sci 235:383\u2013393","journal-title":"Procedia Comput Sci"},{"issue":"16","key":"40_CR64","doi-asserted-by":"crossref","first-page":"47649","DOI":"10.1007\/s11042-023-17434-y","volume":"83","author":"EA Nigus","year":"2024","unstructured":"Nigus EA, Taye GB, Girmaw DW, Salau AO (2024) Development of a model for detection and grading of stem rust in wheat using deep learning. Multimed Tools Appl 83(16):47649\u201347676","journal-title":"Multimed Tools Appl"},{"key":"40_CR65","doi-asserted-by":"crossref","first-page":"887","DOI":"10.3390\/agronomy13030887","volume":"13","author":"MO Ojo","year":"2023","unstructured":"Ojo MO, Zahid A (2023) Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline. Agronomy 13:887","journal-title":"Agronomy"},{"key":"40_CR66","first-page":"277","volume-title":"International conference on smart computing and communication","author":"SL Patil","year":"2024","unstructured":"Patil SL (2024) A high-accuracy deep learning approach for wheat disease detection. International conference on smart computing and communication. Springer Nature Singapore, Singapore, pp 277\u2013291"},{"issue":"1","key":"40_CR67","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s11276-024-03762-w","volume":"31","author":"K Paul Joshua","year":"2025","unstructured":"Paul Joshua K, Alex SA, Mageswari M, Jothilakshmi R (2025) Enhanced conditional self-attention generative adversarial network for detecting cotton plant disease in IoT-enabled crop management. Wirel Netw 31(1):299\u2013313","journal-title":"Wirel Netw"},{"key":"40_CR68","doi-asserted-by":"crossref","unstructured":"Pavithra A, Kalpana G, Vigneswaran T (2023) Deep learning-based automated disease detection and classification model for precision agriculture. Soft Comput 1\u201312","DOI":"10.21203\/rs.3.rs-2263078\/v1"},{"key":"40_CR69","first-page":"440","volume":"12","author":"SE Pawar","year":"2024","unstructured":"Pawar SE, Surana AV, Sharma P, Pujeri R (2024) Fruit disease detection and classification using machine learning and deep learning techniques. Int J Intell Syst Appl Eng 12:440\u2013453","journal-title":"Int J Intell Syst Appl Eng"},{"key":"40_CR70","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.3390\/s150305609","volume":"15","author":"JM Pe\u00f1a","year":"2015","unstructured":"Pe\u00f1a JM, Torres-S\u00e1nchez J, Serrano-P\u00e9rez A, De Castro AI, L\u00f3pez-Granados F (2015) Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15:5609\u20135626","journal-title":"Sensors"},{"key":"40_CR71","first-page":"357","volume":"11","author":"HB Prajapati","year":"2017","unstructured":"Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases. Intell Decis Technol 11:357\u2013373","journal-title":"Intell Decis Technol"},{"key":"40_CR72","doi-asserted-by":"crossref","unstructured":"Prasath B, Akila M, Mohan M (2023) A comprehensive survey on IoT-aided pest detection and classification in agriculture using different image processing techniques.\u00a0Int J Image Graph 2550040","DOI":"10.1142\/S0219467825500408"},{"key":"40_CR73","unstructured":"Pujari P, Pujar P, Choudaki P, Kulkarni P, Doddamani P (2024) Improving plant health using machine learning for image based disease detection. 5(12):3701\u20133705"},{"key":"40_CR74","doi-asserted-by":"crossref","first-page":"114","DOI":"10.3390\/jimaging10050114","volume":"10","author":"R Rahman","year":"2024","unstructured":"Rahman R, Indris C, Bramesfeld G, Zhang T, Li K, Chen X, Grijalva I, McCornack B, Flippo D, Sharda A (2024) A new dataset and comparative study for aphid cluster detection and segmentation in sorghum fields. J Imaging 10:114","journal-title":"J Imaging"},{"key":"40_CR75","doi-asserted-by":"crossref","unstructured":"Raja SN, Nargunam AS (2024) An optimal feature selection-based deep learning approach for wheat disease identification. Multimed Tools Appl 1\u201322","DOI":"10.1007\/s11042-024-19453-9"},{"key":"40_CR76","doi-asserted-by":"crossref","first-page":"109037","DOI":"10.1016\/j.compag.2024.109037","volume":"222","author":"BG Ram","year":"2024","unstructured":"Ram BG, Oduor P, Igathinathane C, Howatt K, Sun X (2024) A systematic review of hyperspectral imaging in precision agriculture: analysis of its current state and future prospects. Comput Electron Agric 222:109037","journal-title":"Comput Electron Agric"},{"key":"40_CR77","doi-asserted-by":"crossref","first-page":"69853","DOI":"10.1109\/ACCESS.2024.3397570","volume":"12","author":"STY Ramadan","year":"2024","unstructured":"Ramadan STY, Sakib T, Al Farid F, Islam MS, Abdullah J, Bhuiyan MR, Karim HA (2024) Improving wheat leaf disease classification: evaluating augmentation strategies and CNN-based models With limited dataset. IEEE Access 12:69853\u201369874","journal-title":"IEEE Access"},{"issue":"1","key":"40_CR78","doi-asserted-by":"crossref","first-page":"2200216","DOI":"10.1002\/masy.202200216","volume":"413","author":"L Rani","year":"2024","unstructured":"Rani L, Sarangi PK, Sahoo AK (2024) Image-feature based deep learning model for plant leaf disease detection. Macromol Symp 413(1):2200216","journal-title":"Macromol Symp"},{"key":"40_CR79","doi-asserted-by":"crossref","unstructured":"Reddy YA, Adimoolam M (2024) Efficient plant leaf disease detection using support vector machine algorithm and compare its features with Naive Bayes classification. In: AIP conference proceedings, vol 2729. AIP Publishing","DOI":"10.1063\/5.0174001"},{"key":"40_CR80","doi-asserted-by":"crossref","first-page":"109790","DOI":"10.1016\/j.microc.2023.109790","volume":"197","author":"HC Reis","year":"2024","unstructured":"Reis HC, Turk V (2024) Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection. Microchem J 197:109790","journal-title":"Microchem J"},{"key":"40_CR81","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.3390\/electronics13061010","volume":"13","author":"G Routis","year":"2024","unstructured":"Routis G, Michailidis M, Roussaki I (2024) Plant disease identification using machine learning algorithms on single-board computers in IoT environments. Electronics 13:1010","journal-title":"Electronics"},{"key":"40_CR82","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.pce.2018.12.004","volume":"112","author":"L Royimani","year":"2019","unstructured":"Royimani L, Mutanga O, Odindi J, Dube T, Matongera TN (2019) Advancements in satellite remote sensing for mapping and monitoring of alien invasive plant species (AIPs). Phys Chem Earth Parts A\/B\/C 112:237\u2013245","journal-title":"Phys Chem Earth Parts A\/B\/C"},{"key":"40_CR83","volume":"38","author":"P Sajitha","year":"2024","unstructured":"Sajitha P, Andrushia AD, Anand N, Naser MZ (2024) A review on machine learning and deep learning image-based plant disease classification for industrial farming systems. J Ind Inf Integr 38:100572","journal-title":"J Ind Inf Integr"},{"key":"40_CR84","doi-asserted-by":"crossref","unstructured":"Saraswat S, Batra S, Neog PP, Sharma EL, Kumar PP, Pandey AK (2024) An efficient diagnostic approach for multi-class classification of wheat leaf disease using deep transfer and ensemble learning. In: 2024 2nd international conference on intelligent data communication technologies and internet of things (IDCIoT). IEEE,\u00a0pp 544\u2013551","DOI":"10.1109\/IDCIoT59759.2024.10467803"},{"issue":"2","key":"40_CR85","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s41348-024-00876-3","volume":"131","author":"E Saraswathi","year":"2024","unstructured":"Saraswathi E, Faritha Banu J (2024) A novel probabilistic intermittent neural network (PINN) and artificial jelly fish optimization (AJFO)-based plant leaf disease detection system. J Plant Dis Prot 131(2):587\u2013600","journal-title":"J Plant Dis Prot"},{"key":"40_CR86","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110534","volume":"145","author":"C Sarkar","year":"2023","unstructured":"Sarkar C, Gupta D, Gupta U, Hazarika BB (2023) Leaf disease detection using machine learning and deep learning: review and challenges. Appl Soft Comput 145:110534","journal-title":"Appl Soft Comput"},{"key":"40_CR87","first-page":"281","volume":"12","author":"V Saxena","year":"2024","unstructured":"Saxena V, Singh M, Saxena P, Singh M, Srivastava AP, Kumar N, Deepak A, Shrivastava A (2024) Utilizing support vector machines for early detection of crop diseases in precision agriculture a data mining perspective. Int J Intell Syst Appl Eng 12:281\u2013288","journal-title":"Int J Intell Syst Appl Eng"},{"key":"40_CR88","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/978-3-031-43548-5_10","volume-title":"Digital agriculture: a solution for sustainable food and nutritional security","author":"J Segarra","year":"2024","unstructured":"Segarra J (2024) Satellite imagery in precision agriculture. In: Digital agriculture: a solution for sustainable food and nutritional security. Springer International Publishing, Cham, pp 325\u2013340"},{"key":"40_CR89","doi-asserted-by":"crossref","first-page":"23726","DOI":"10.1109\/ACCESS.2023.3254430","volume":"11","author":"U Shafi","year":"2023","unstructured":"Shafi U, Mumtaz R, Qureshi MDM, Mahmood Z, Tanveer SK, Haq IU, Zaidi SMH (2023) Embedded AI for wheat yellow rust infection type classification. IEEE Access 11:23726\u201323738","journal-title":"IEEE Access"},{"key":"40_CR90","doi-asserted-by":"crossref","first-page":"59174","DOI":"10.1109\/ACCESS.2023.3284760","volume":"11","author":"W Shafik","year":"2023","unstructured":"Shafik W, Tufail A, Namoun A, De Silva LC, Apong RAAHM (2023) A systematic literature review on plant disease detection: motivations, classification techniques, datasets, challenges, and future trends. Ieee Access 11:59174\u201359203","journal-title":"Ieee Access"},{"key":"40_CR91","first-page":"1","volume":"5","author":"T Sharma","year":"2024","unstructured":"Sharma T, Sethi GK (2024) Improving wheat leaf disease image classification with point rend segmentation technique. SN Comput Sci 5:1\u201312","journal-title":"SN Comput Sci"},{"key":"40_CR92","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1080\/0952813X.2022.2096698","volume":"36","author":"G Sharma","year":"2024","unstructured":"Sharma G, Kumar A, Gour N, Saini AK, Upadhyay A, Kumar A (2024) Cognitive framework and learning paradigms of plant leaf classification using artificial neural network and support vector machine. J Exp Theor Artif Intell 36:585\u2013610","journal-title":"J Exp Theor Artif Intell"},{"key":"40_CR93","doi-asserted-by":"crossref","unstructured":"Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N (2020) PlantDoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. pp 249\u2013253","DOI":"10.1145\/3371158.3371196"},{"key":"40_CR94","doi-asserted-by":"crossref","first-page":"2160831","DOI":"10.1080\/10106049.2022.2160831","volume":"38","author":"R Singh","year":"2023","unstructured":"Singh R, Krishnan P, Singh VK, Das B (2023) Estimation of yellow rust severity in wheat using visible and thermal imaging coupled with machine learning models. Geocarto Int 38:2160831","journal-title":"Geocarto Int"},{"issue":"5","key":"40_CR95","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1007\/s00217-024-04473-4","volume":"250","author":"ME Sonmez","year":"2024","unstructured":"Sonmez ME, Sabanci K, Aydin N (2024) Convolutional neural network-support vector machine-based approach for identification of wheat hybrids. Eur Food Res Technol 250(5):1353\u20131362","journal-title":"Eur Food Res Technol"},{"issue":"6","key":"40_CR96","first-page":"320","volume":"12","author":"S Sridevy","year":"2023","unstructured":"Sridevy S, Devi MN, Sankar M, Moorthi NR (2023) Image pre-processing techniques utilized for the plant identification: A review. Pharma Innovation 12(6):320\u2013324","journal-title":"Pharma Innovation"},{"key":"40_CR97","doi-asserted-by":"crossref","first-page":"38411","DOI":"10.1007\/s11042-023-16929-y","volume":"83","author":"M Srivastava","year":"2024","unstructured":"Srivastava M, Meena J (2024) Plant leaf disease detection and classification using modified transfer learning models. Multimed Tools Appl 83:38411\u201338441","journal-title":"Multimed Tools Appl"},{"key":"40_CR98","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/978-1-0716-3790-6_19","volume-title":"Photosynthesis: methods and protocols","author":"J Stamford","year":"2024","unstructured":"Stamford J, Aciksoz SB, Lawson T (2024) Remote sensing techniques: hyperspectral imaging and data analysis. In: Photosynthesis: methods and protocols. Springer, New York, NY, pp 373\u2013390"},{"key":"40_CR99","first-page":"145","volume":"16","author":"A Stephen","year":"2024","unstructured":"Stephen A, Arumugam P, Arumugam C (2024) An efficient deep learning with a big data-based cotton plant monitoring system. Int J Inf Technol 16:145\u2013151","journal-title":"Int J Inf Technol"},{"key":"40_CR100","doi-asserted-by":"crossref","unstructured":"Subbiah P, Krishnaraj N (2024) Automated plant disease detection systems for the smart farming sector. In: Agriculture and aquaculture applications of biosensors and bioelectronics; IGI global, pp 296\u2013309","DOI":"10.4018\/979-8-3693-2069-3.ch015"},{"key":"40_CR101","doi-asserted-by":"crossref","unstructured":"Subbiah P, Nagappan K (2024) Plant leaf disease detection using metaheuristic optimization algorithms and deep learning.\u00a0Rev Intell Artif 38(2)","DOI":"10.18280\/ria.380216"},{"key":"40_CR102","doi-asserted-by":"crossref","first-page":"107709","DOI":"10.1016\/j.compag.2023.107709","volume":"207","author":"Z Tang","year":"2023","unstructured":"Tang Z, Wang M, Schirrmann M, Dammer K-H, Li X, Brueggeman R, Sankaran S, Carter AH, Pumphrey MO, Hu Y (2023) Affordable high throughput field detection of wheat stripe rust using deep learning with semi-automated image labeling. Comput Electron Agric 207:107709","journal-title":"Comput Electron Agric"},{"key":"40_CR103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-023-12132-6","volume":"196","author":"D Thakur","year":"2024","unstructured":"Thakur D, Srinivasan S (2024) AI-PUCMDL: artificial intelligence assisted plant counting through unmanned aerial vehicles in India\u2019s mountainous regions. Environ Monit Assess 196:1\u201326","journal-title":"Environ Monit Assess"},{"key":"40_CR104","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1080\/00051144.2024.2317093","volume":"65","author":"R Thivya Lakshmi","year":"2024","unstructured":"Thivya Lakshmi R, Katiravan J, Visu P (2024) CoDet: a novel deep learning pipeline for cotton plant detection and disease identification. Automatika 65:662\u2013674","journal-title":"Automatika"},{"issue":"23","key":"40_CR105","doi-asserted-by":"crossref","first-page":"63121","DOI":"10.1007\/s11042-023-18030-w","volume":"83","author":"A Tiendrebeogo","year":"2024","unstructured":"Tiendrebeogo A (2024) Identification of plants from the convolutional neural network. Multimed Tools Appl 83(23):63121\u201363131","journal-title":"Multimed Tools Appl"},{"key":"40_CR106","doi-asserted-by":"crossref","first-page":"81","DOI":"10.61356\/j.oia.2024.1257","volume":"1","author":"A Tolba","year":"2024","unstructured":"Tolba A, Talal N (2024) An interpretable deep learning for early detection and diagnosis of wheat leaf diseases. Optim Agric 1:81\u201393","journal-title":"Optim Agric"},{"issue":"23","key":"40_CR107","doi-asserted-by":"crossref","first-page":"62875","DOI":"10.1007\/s11042-023-18049-z","volume":"83","author":"A Usha Ruby","year":"2024","unstructured":"Usha Ruby A, George Chellin Chandran J, Chaithanya BN, Swasthika Jain TJ, Patil R (2024) Wheat leaf disease classification using modified ResNet50 convolutional neural network model. Multimed Tools Appl 83(23):62875\u201362893","journal-title":"Multimed Tools Appl"},{"key":"40_CR108","doi-asserted-by":"crossref","first-page":"0153","DOI":"10.34133\/plantphenomics.0153","volume":"6","author":"JJ Walsh","year":"2024","unstructured":"Walsh JJ, Mangina E, Negr\u00e3o S (2024) Advancements in imaging sensors and ai for plant stress detection: a systematic literature review. Plant Phenomics 6:0153","journal-title":"Plant Phenomics"},{"key":"40_CR109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13104-018-3548-6","volume":"11","author":"T Wiesner-Hanks","year":"2018","unstructured":"Wiesner-Hanks T, Stewart EL, Kaczmar N, DeChant C, Wu H, Nelson RJ, Lipson H, Gore MA (2018) Image set for deep learning: field images of maize annotated with disease symptoms. BMC Res Notes 11:1\u20133","journal-title":"BMC Res Notes"},{"key":"40_CR110","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.3390\/app14104118","volume":"14","author":"J Xue","year":"2024","unstructured":"Xue J, Qian X, Kang DH, Hunter JG (2024) Plant density and health evaluation in green stormwater infrastructure using unmanned-aerial-vehicle-based imagery. Appl Sci 14:4118","journal-title":"Appl Sci"},{"key":"40_CR111","doi-asserted-by":"crossref","unstructured":"Zenkl R, McDonald BA, Walter A, Anderegg J (2024) Towards high throughput in-field detection and quantification of wheat foliar diseases with deep learning. bioRxiv, 2024-05","DOI":"10.1101\/2024.05.10.593608"},{"key":"40_CR112","doi-asserted-by":"crossref","first-page":"398","DOI":"10.3390\/drones7060398","volume":"7","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Zhu L (2023) A review on unmanned aerial vehicle remote sensing: platforms, sensors, data processing methods, and applications. Drones 7:398","journal-title":"Drones"},{"key":"40_CR113","doi-asserted-by":"crossref","first-page":"104943","DOI":"10.1016\/j.compag.2019.104943","volume":"165","author":"J Zhang","year":"2019","unstructured":"Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K, Huang W (2019) Monitoring plant diseases and pests through remote sensing technology: a review. Comput Electron Agric 165:104943","journal-title":"Comput Electron Agric"},{"key":"40_CR114","doi-asserted-by":"crossref","first-page":"109086","DOI":"10.1016\/j.compag.2024.109086","volume":"222","author":"S Zhang","year":"2024","unstructured":"Zhang S, Liu Y, Xiong K, Tian Y, Du Y, Zhu Z, Du M, Zhai Z (2024) A review of vision-based crop row detection method: focusing on field ground autonomous navigation operations. Comput Electron Agric 222:109086","journal-title":"Comput Electron Agric"},{"key":"40_CR115","doi-asserted-by":"crossref","first-page":"110208","DOI":"10.1016\/j.ress.2024.110208","volume":"249","author":"S Zhao","year":"2024","unstructured":"Zhao S, Duan Y, Roy N, Zhang B (2024) A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis. Reliab Eng Syst Saf 249:110208","journal-title":"Reliab Eng Syst Saf"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00040-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00040-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00040-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T11:46:29Z","timestamp":1748605589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00040-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":115,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["40"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00040-3","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"34"}}