{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T08:02:17Z","timestamp":1789977737039,"version":"4.0.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Chongqing Three Gorges Vocational College","award":["cqsx2025006"],"award-info":[{"award-number":["cqsx2025006"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-026-02274-0","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T07:04:10Z","timestamp":1789974250000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid transformer with spatiotemporal attention for citrus pest and disease forecasting"],"prefix":"10.1007","volume":"6","author":[{"given":"Changning","family":"Ji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"2274_CR1","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1038\/s41559-018-0793-y","volume":"3","author":"S Savary","year":"2019","unstructured":"Savary S, Willocquet L, Pethybridge SJ. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019;3:430\u20139. https:\/\/doi.org\/10.1038\/s41559-018-0793-y.","journal-title":"Nat Ecol Evol"},{"key":"2274_CR2","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s12571-017-0659-1","volume":"9","author":"S Savary","year":"2017","unstructured":"Savary S, Bregaglio S, Willocquet L. Crop health and its global impacts on the components of food security. Food Sec. 2017;9:311\u201327. https:\/\/doi.org\/10.1007\/s12571-017-0659-1.","journal-title":"Food Sec"},{"key":"2274_CR3","volume-title":"Breeding insect resistant crops for sustainable agriculture","author":"S Sharma","year":"2017","unstructured":"Sharma S, Kooner R, Arora R. Insect pests and crop losses. In: Arora R, Sandhu S, editors. Breeding insect resistant crops for sustainable agriculture. Singapore: Springer; 2017."},{"key":"2274_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/s41893-019-0450-8","volume":"3","author":"A Varah","year":"2020","unstructured":"Varah A, Ahodo K, Coutts SR. The costs of human-induced evolution in an agricultural system. Nat Sustain. 2020;3:63\u201371. https:\/\/doi.org\/10.1038\/s41893-019-0450-8.","journal-title":"Nat Sustain"},{"key":"2274_CR5","volume-title":"Emerging contaminants and plants","author":"BA Khan","year":"2023","unstructured":"Khan BA. Pesticides: impacts on agriculture productivity, environment, and management strategies. In: Aftab T, editor. Emerging contaminants and plants. Cham: Springer; 2023."},{"key":"2274_CR6","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1023\/A:1026018005613","volume":"109","author":"A Maanen","year":"2003","unstructured":"Maanen A, Xu X-M. Modelling plant disease epidemics. Eur J Plant Pathol. 2003;109:669\u201382. https:\/\/doi.org\/10.1023\/A:1026018005613.","journal-title":"Eur J Plant Pathol"},{"key":"2274_CR7","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1111\/j.1365-2338.2007.01125.x","volume":"37","author":"V Rossi","year":"2007","unstructured":"Rossi V, Giosue S, Bugiani R. A-scab (Apple-scab), a simulation model for estimating risk of Venturia inaequalis primary infections. OEPP\/EPPO Bull. 2007;37:300\u20138. https:\/\/doi.org\/10.1111\/j.1365-2338.2007.01125.x.","journal-title":"OEPP\/EPPO Bull"},{"issue":"4","key":"2274_CR8","doi-asserted-by":"publisher","first-page":"454","DOI":"10.18782\/2582-2845.8280","volume":"8","author":"RK Nath","year":"2020","unstructured":"Nath RK, Begum KH, Choudhury MR. Plant disease forecasting models. Ind J Pure App Biosci. 2020;8(4):454\u201361. https:\/\/doi.org\/10.18782\/2582-2845.8280.","journal-title":"Ind J Pure App Biosci"},{"issue":"2","key":"2274_CR9","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/S1226-8615(08)60113-2","volume":"4","author":"JS Kim","year":"2001","unstructured":"Kim JS, Jung C, Lee JH. Parameter estimation for temperature-dependent development model of Tetranychus urticae Koch: immature development. J Asia-Pacific Entomol. 2001;4(2):123\u20139. https:\/\/doi.org\/10.1016\/S1226-8615(08)60113-2.","journal-title":"J Asia-Pacific Entomol"},{"issue":"3","key":"2274_CR10","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/S1226-8615(08)60247-2","volume":"8","author":"C Jung","year":"2005","unstructured":"Jung C, Kim D-S, Park Y-S, Lee J-H. Simulation modeling of twospotted spider mite population dynamics in apple and pear orchards in Korea. J Asia-Pacific Entomol. 2005;8(3):285\u201390. https:\/\/doi.org\/10.1016\/S1226-8615(08)60247-2.","journal-title":"J Asia-Pacific Entomol"},{"issue":"1","key":"2274_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2022.115365","volume":"317","author":"S Bregaglio","year":"2022","unstructured":"Bregaglio S. A public decision support system for the assessment of plant disease infection risk shared by Italian regions. J Environ Manage. 2022;317(1):115365. https:\/\/doi.org\/10.1016\/j.jenvman.2022.115365.","journal-title":"J Environ Manage"},{"key":"2274_CR12","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/ps.2780360210","volume":"36","author":"G Edwards-Jones","year":"1992","unstructured":"Edwards-Jones G. Knowledge-based systems for pest management: an applications-based review. Pestic Sci. 1992;36:143\u201353. https:\/\/doi.org\/10.1002\/ps.2780360210.","journal-title":"Pestic Sci"},{"key":"2274_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2020.102865","author":"A Agatz","year":"2020","unstructured":"Agatz A, Ashauer R, Sweeney P, Brown CD. A knowledge-based approach to designing control strategies for agricultural pests. Agric Syst. 2020. https:\/\/doi.org\/10.1016\/j.agsy.2020.102865.","journal-title":"Agric Syst"},{"key":"2274_CR14","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-3059.2011.02498.x","author":"SE Legler","year":"2011","unstructured":"Legler SE, Caffi T, Rossi V. A nonlinear model for temperature-dependent development of Erysiphe necator chasmothecia on grapevine leaves. Plant Pathol. 2011. https:\/\/doi.org\/10.1111\/j.1365-3059.2011.02498.x.","journal-title":"Plant Pathol"},{"key":"2274_CR15","doi-asserted-by":"publisher","DOI":"10.1080\/09670874.2016.1210839","author":"BP Singh","year":"2016","unstructured":"Singh BP, Govindakrishnan PM, Ahmad I, Rawat S, Sharma S. Sreekumar J. INDO-BLIGHTCAST: a model for forecasting late blight across agroecologies. Int J Pest Manag. 2016. https:\/\/doi.org\/10.1080\/09670874.2016.1210839.","journal-title":"Int J Pest Manag"},{"key":"2274_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105028","volume":"166","author":"A Khattab","year":"2019","unstructured":"Khattab A, Habib SE, Ismail H, Zayan S, Fahmy Y, Khairy MM. An IoT-based cognitive monitoring system for early plant disease forecast. Comput Electron Agric. 2019;166:105028. https:\/\/doi.org\/10.1016\/j.compag.2019.105028.","journal-title":"Comput Electron Agric"},{"key":"2274_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s11119-022-09927-x","volume":"24","author":"I Bhakta","year":"2023","unstructured":"Bhakta I, Phadikar S, Majumder K. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agric. 2023;24:23\u201339. https:\/\/doi.org\/10.1007\/s11119-022-09927-x.","journal-title":"Precision Agric"},{"key":"2274_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fenvs.2018.00063","author":"NK Newlands","year":"2018","unstructured":"Newlands NK. Model-based forecasting of agricultural crop disease risk at the regional scale, integrating airborne inoculum, environmental, and satellite-based monitoring data. Front Environ Sci. 2018. https:\/\/doi.org\/10.3389\/fenvs.2018.00063.","journal-title":"Front Environ Sci"},{"key":"2274_CR19","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.compag.2019.02.007","volume":"158","author":"MS Mahmud","year":"2019","unstructured":"Mahmud MS, Zaman QU, Esau TJ, Price GW, Prithiviraj B. Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection. Comput Electron Agric. 2019;158:219\u201325. https:\/\/doi.org\/10.1016\/j.compag.2019.02.007.","journal-title":"Comput Electron Agric"},{"key":"2274_CR20","doi-asserted-by":"publisher","first-page":"1051730","DOI":"10.3389\/fmicb.2022.1051730","volume":"13","author":"D Yong","year":"2022","unstructured":"Yong D, Li Y, Gong K. Biocontrol of strawberry gray mold caused by Botrytis cinerea with the termite associated Streptomyces sp. sdu1201 and actinomycin D. Front Microbiol. 2022;13:1051730. https:\/\/doi.org\/10.3389\/fmicb.2022.1051730.","journal-title":"Front Microbiol"},{"key":"2274_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2015.00385","author":"JR Lamichhane","year":"2015","unstructured":"Lamichhane JR, Venturi V. Synergism between microbial pathogens in plant disease complexes: a growing trend. Front Plant Sci. 2015. https:\/\/doi.org\/10.3389\/fpls.2015.00385.","journal-title":"Front Plant Sci"},{"key":"2274_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110534","volume":"145","author":"C Sarkar","year":"2023","unstructured":"Sarkar C, Gupta D, Gupta U, Hazarika BB. Leaf disease detection using machine learning and deep learning: review and challenges. Appl Soft Comput. 2023;145:110534. https:\/\/doi.org\/10.1016\/j.asoc.2023.110534.","journal-title":"Appl Soft Comput"},{"key":"2274_CR23","doi-asserted-by":"publisher","first-page":"1079366","DOI":"10.3389\/fpls.2023.1079366","volume":"14","author":"F Khan","year":"2023","unstructured":"Khan F, Zafar N, Tahir MN, Aqib M, Waheed H, Haroon Z. A mobile-based system for maize plant leaf disease detection and classification using deep learning. Front Plant Sci. 2023;14:1079366. https:\/\/doi.org\/10.3389\/fpls.2023.1079366.","journal-title":"Front Plant Sci"},{"issue":"3","key":"2274_CR24","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/03235408.2023.2183792","volume":"56","author":"G Singh","year":"2023","unstructured":"Singh G, Yogi KK. Performance evaluation of plant leaf disease detection using deep learning models. Arch Phytopathol Plant Protect. 2023;56(3):209\u201333. https:\/\/doi.org\/10.1080\/03235408.2023.2183792.","journal-title":"Arch Phytopathol Plant Protect"},{"issue":"2","key":"2274_CR25","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1080\/10807039.2022.2064814","volume":"29","author":"R Mahum","year":"2023","unstructured":"Mahum R, Munir H, Mughal ZUN. A novel framework for potato leaf disease detection using an efficient deep learning model. Hum Ecol Risk Assess Int J. 2023;29(2):303\u201326. https:\/\/doi.org\/10.1080\/10807039.2022.2064814.","journal-title":"Hum Ecol Risk Assess Int J"},{"issue":"2","key":"2274_CR26","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/s42979-022-01589-w","volume":"4","author":"R Ramamoorthy","year":"2023","unstructured":"Ramamoorthy R, Saravana Kumar E, Naidu RCA, Shruthi K. Reliable and accurate plant leaf disease detection with treatment suggestions using enhanced deep learning techniques. SN Comput Sci. 2023;4(2):158. https:\/\/doi.org\/10.1007\/s42979-022-01589-w.","journal-title":"SN Comput Sci"},{"issue":"2","key":"2274_CR27","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/computation11020020","volume":"11","author":"IN Yulita","year":"2023","unstructured":"Yulita IN, Amri NA, Hidayat A. Mobile application for tomato plant leaf disease detection using a dense convolutional network architecture. Computation. 2023;11(2):20. https:\/\/doi.org\/10.3390\/computation11020020.","journal-title":"Computation"},{"key":"2274_CR28","doi-asserted-by":"publisher","first-page":"23149","DOI":"10.1109\/ACCESS.2024.3357099","volume":"12","author":"R Rashid","year":"2024","unstructured":"Rashid R, Aslam W, Aziz R, Aldehim G. An early and smart detection of corn plant leaf diseases using IoT and deep learning multi-models. IEEE Access. 2024;12:23149\u201362. https:\/\/doi.org\/10.1109\/ACCESS.2024.3357099.","journal-title":"IEEE Access"},{"key":"2274_CR29","doi-asserted-by":"publisher","first-page":"3500","DOI":"10.1016\/j.matpr.2021.07.281","volume":"80","author":"AV Panchal","year":"2023","unstructured":"Panchal AV, Patel SC, Bagyalakshmi K, Kumar P, Khan IR, Soni M. Image-based plant diseases detection using deep learning. Mater Today Proc. 2023;80:3500\u20136. https:\/\/doi.org\/10.1016\/j.matpr.2021.07.281.","journal-title":"Mater Today Proc"},{"issue":"11","key":"2274_CR30","doi-asserted-by":"publisher","first-page":"6871","DOI":"10.1007\/s00034-023-02430-2","volume":"42","author":"PV Yeswanth","year":"2023","unstructured":"Yeswanth PV, Deivalakshmi S, George S, Ko SB. Residual skip network-based super-resolution for leaf disease detection of grape plant. Circ Syst Signal Process. 2023;42(11):6871\u201399. https:\/\/doi.org\/10.1007\/s00034-023-02430-2.","journal-title":"Circ Syst Signal Process"},{"issue":"8","key":"2274_CR31","doi-asserted-by":"publisher","first-page":"12065","DOI":"10.1007\/s11042-022-13737-8","volume":"82","author":"H Mustafa","year":"2023","unstructured":"Mustafa H, Umer M, Hafeez U. Pepper bell leaf disease detection and classification using optimized convolutional neural network. Multimed Tools Appl. 2023;82(8):12065\u201380. https:\/\/doi.org\/10.1007\/s11042-022-13737-8.","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"2274_CR32","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.32604\/cmc.2023.032005","volume":"74","author":"J Rashid","year":"2023","unstructured":"Rashid J, Khan I, Ali G, Alturise F, Alkhalifah T. Real-time multiple guava leaf disease detection from a single leaf using hybrid deep learning technique. Comput Mater Continua. 2023;74(1):1235. https:\/\/doi.org\/10.32604\/cmc.2023.032005.","journal-title":"Comput Mater Continua"},{"issue":"9","key":"2274_CR33","doi-asserted-by":"publisher","first-page":"12407","DOI":"10.1007\/s12652-022-04331-9","volume":"14","author":"P Kaur","year":"2023","unstructured":"Kaur P, Harnal S, Gautam V, Singh MP, Singh SP. A novel transfer deep learning method for detection and classification of plant leaf disease. J Ambient Intell Humaniz Comput. 2023;14(9):12407\u201324. https:\/\/doi.org\/10.1007\/s12652-022-04331-9.","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"13","key":"2274_CR34","doi-asserted-by":"publisher","first-page":"38209","DOI":"10.1007\/s11042-023-16882-w","volume":"83","author":"JG Kotwal","year":"2024","unstructured":"Kotwal JG, Kashyap R, Shafi PM. Artificial driving based EfficientNet for automatic plant leaf disease classification. Multimed Tools Appl. 2024;83(13):38209\u201340. https:\/\/doi.org\/10.1007\/s11042-023-16882-w.","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"2274_CR35","doi-asserted-by":"publisher","first-page":"29912","DOI":"10.1016\/j.heliyon.2024.e29912","volume":"10","author":"S Vallabhajosyula","year":"2024","unstructured":"Vallabhajosyula S, Sistla V, Kolli VKK. A novel hierarchical framework for plant leaf disease detection using residual vision transformer. Heliyon. 2024;10(9):29912. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e29912.","journal-title":"Heliyon"},{"issue":"1","key":"2274_CR36","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s41870-023-01647-3","volume":"16","author":"S Kusuma","year":"2024","unstructured":"Kusuma S, Jothi KR. Early betel leaf disease detection using vision transformer and deep learning algorithms. Int J Inf Technol. 2024;16(1):169\u201380. https:\/\/doi.org\/10.1007\/s41870-023-01647-3.","journal-title":"Int J Inf Technol"},{"issue":"10","key":"2274_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-025-07623-x","volume":"81","author":"JW Cui","year":"2025","unstructured":"Cui JW, Yuan CH. Multi-scale feature aggregation with hierarchical semantics and uncertainty assessment: enabling high-accuracy visual retrieval. J Supercomput. 2025;81(10):1\u201326. https:\/\/doi.org\/10.1007\/s11227-025-07623-x.","journal-title":"J Supercomput"},{"key":"2274_CR38","doi-asserted-by":"publisher","DOI":"10.1080\/03055698.2025.2531969","author":"N Xiao","year":"2025","unstructured":"Xiao N. A study of artificial intelligence in writing assessment for secondary school students: a comparative analysis based on the GPT-4 and human raters. Educ Stud. 2025. https:\/\/doi.org\/10.1080\/03055698.2025.2531969.","journal-title":"Educ Stud"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-02274-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-026-02274-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-02274-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T07:04:11Z","timestamp":1789974251000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-026-02274-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2274"],"URL":"https:\/\/doi.org\/10.1007\/s44163-026-02274-0","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]},"assertion":[{"value":"14 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"1202"}}