{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T16:21:04Z","timestamp":1787674864360,"version":"build-2784847793"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Plant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks. Detection accuracy, precision, recall, F1-score, and area under the curve remain indispensable, but they are insufficient for judging whether a warning can support timely and proportionate phytoprotection under field variability. This Mini Review argues that intelligent plant health warning systems should be evaluated not only as prediction models, but also as safety-relevant decision-support systems embedded in biological, agronomic, and operational contexts. We first relate AI-based detection to established plant disease forecasting and decision-support traditions, including weather-based models, epidemiological forecasting, and integrated disease management. We then adapt selected safety-assurance concepts, including risk assessment, failure mode and effects analysis, Bow-tie reasoning, warning-threshold governance, reliability analysis, resilience thinking, and response closure, to host-pathogen-environment warning chains. The proposed framework links AI or sensor outputs with pathogen biology, host susceptibility, environmental conduciveness, inoculum pressure, uncertainty assessment, risk classification, threshold decisions, human or automated verification, intervention, and feedback learning. Illustrative crop-pathogen scenarios, including wheat rust, rice blast, potato late blight, grapevine downy mildew, and citrus greening, show how safety assurance can complement existing forecasting and decision-support systems rather than replace them. The framework remains conceptual, and whether these added assurance functions improve existing warning systems requires comparative evaluation under field conditions. Future systems should be evaluated through detection performance and response-oriented indicators such as lead time, calibration, false-alert burden, missed-warning rate, response completion, disease suppression, economic value, and learning after field action.<\/jats:p>","DOI":"10.3389\/fpls.2026.1899310","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:39:35Z","timestamp":1787672375000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From detection accuracy to safety assurance in intelligent plant health early warning systems"],"prefix":"10.3389","volume":"17","author":[{"given":"Junjie","family":"Wei","sequence":"first","affiliation":[{"name":"School of Intelligent Engineering, Shandong Management University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyang","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering, Shandong Management University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering, Shandong Management University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: the new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.biosystemseng.2016.01.017","article-title":"A review on the main challenges in automatic plant disease identification based on visible range images","volume":"144","author":"Barbedo","year":"2016","journal-title":"Biosyst. Eng."},{"key":"B3","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Barredo Arrieta","year":"2020","journal-title":"Inf. Fusion."},{"key":"B4","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1080\/07060660509507230","article-title":"Epidemiology and control of stripe rust [Puccinia striiformis f. sp. tritici] on wheat","volume":"27","author":"Chen","year":"2005","journal-title":"Can. J. Plant Pathol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.ssci.2016.03.001","article-title":"The bowtie method: A review","volume":"88","author":"de Ruijter","year":"2016","journal-title":"Saf. Sci."},{"key":"B6","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1094\/PHP-03-23-0027-RP","article-title":"Recovery plan for Ralstonia solanacearum Race 3 Biovar 2 (Phylotype IIB, sequevars 1 and 2) causing brown rot of potato, bacterial wilt of tomato, and southern wilt of geranium","volume":"25","author":"Dickstein","year":"2024","journal-title":"Plant Health Prog."},{"key":"B7","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","article-title":"Phenomics - technologies to relieve the phenotyping bottleneck","volume":"16","author":"Furbank","year":"2011","journal-title":"Trends Plant Sci."},{"key":"B8","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1111\/j.1365-3059.2010.02409.x","article-title":"Complexity in climate-change impacts: an analytical framework for effects mediated by plant disease","volume":"60","author":"Garrett","year":"2011","journal-title":"Plant Pathol."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","article-title":"A survey of uncertainty in deep neural networks","volume":"56","author":"Gawlikowski","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"B10","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1146\/annurev-phyto-073009-114418","article-title":"Current epidemiological understanding of citrus Huanglongbing","volume":"48","author":"Gottwald","year":"2010","journal-title":"Annu. Rev. Phytopathol."},{"key":"B11","first-page":"1321","article-title":"On calibration of modern neural networks","author":"Guo","year":"2017"},{"key":"B12","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/s10658-024-02878-1","article-title":"A framework for evaluating the value of agricultural pest management decision support systems","volume":"169","author":"Helps","year":"2024","journal-title":"Eur. J. Plant Pathol."},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1201\/9781315607511","volume-title":"Safety-I and Safety-II: The Past and Future of Safety Management","author":"Hollnagel","year":"2018"},{"key":"B14","volume-title":"Resilience Engineering: Concepts and Precepts","author":"Hollnagel","year":"2006"},{"key":"B15","volume-title":"IEC 60812:2018 Failure modes and effects analysis (FMEA and FMECA)","year":"2018"},{"key":"B16","volume-title":"IEC 31010:2019 Risk management-Risk assessment techniques","year":"2019"},{"key":"B17","volume-title":"ISO 31000:2018 risk management - guidelines","year":"2018"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1356260","article-title":"Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations","volume":"15","author":"Jafar","year":"2024","journal-title":"Front. Plant Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"B21","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1186\/s12859-019-3065-1","article-title":"Predicting rice blast disease: machine learning versus process-based models","volume":"20","author":"Nettleton","year":"2019","journal-title":"BMC Bioinf."},{"key":"B22","article-title":"Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift","author":"Ovadia","year":"2019"},{"key":"B23","doi-asserted-by":"publisher","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":"B24","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.compag.2013.10.011","article-title":"Addressing the implementation problem in agricultural decision support systems: the example of vite.net","volume":"100","author":"Rossi","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"B25","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1038\/s41559-018-0793-y","article-title":"The global burden of pathogens and pests on major food crops","volume":"3","author":"Savary","year":"2019","journal-title":"Nat. Ecol. Evol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vision"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1538163","article-title":"Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions","volume":"16","author":"Shoaib","year":"2025","journal-title":"Front. Plant Sci."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1146\/annurev-phyto-082712-102244","article-title":"Will decision-support systems be widely used for the management of plant diseases","volume":"51","author":"Shtienberg","year":"2013","journal-title":"Annu. Rev. Phytopathol."},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3390\/rs12193136","article-title":"Applications of remote sensing in precision agriculture: A review","volume":"12","author":"Sishodia","year":"2020","journal-title":"Remote Sens."},{"key":"B30","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.compag.2015.05.010","article-title":"Development and implementation of the BlightPro decision support system for potato and tomato late blight management","volume":"115","author":"Small","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"B31","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1146\/annurev.phyto.43.113004.133839","article-title":"Plant disease: a threat to global food security","volume":"43","author":"Strange","year":"2005","journal-title":"Annu. Rev. Phytopathol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s10462-024-11100-x","article-title":"Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture","volume":"58","author":"Upadhyay","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"B33","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s10681-011-0360-y","article-title":"Global status of stripe rust: a review of historical and current threats","volume":"179","author":"Wellings","year":"2011","journal-title":"Euphytica"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2025.10.032","article-title":"Recent developments and applications of crop disease detection, prediction, and early warning: A review","volume":"62","author":"Yan","year":"2026","journal-title":"Engineering"},{"key":"B35","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1046\/j.0032-0862.2002.00741.x","article-title":"Bayesian analysis of plant disease prediction","volume":"51","author":"Yuen","year":"2002","journal-title":"Plant Pathol."},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1637241","article-title":"A review of plant leaf disease identification by deep learning algorithms","volume":"16","author":"Zhao","year":"2025","journal-title":"Front. Plant Sci."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1899310\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:39:37Z","timestamp":1787672377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1899310\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,25]]},"references-count":36,"alternative-id":["10.3389\/fpls.2026.1899310"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1899310","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,25]]},"article-number":"1899310"}}