{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:13Z","timestamp":1788419773563,"version":"build-2803163510"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Tomato (\n                      <jats:italic>Solanum lycopersicum L<\/jats:italic>\n                      .) bacterial wilt is a highly destructive soil-borne disease whose early-stage infections are asymptomatic or visually indistinguishable, limiting the effectiveness of conventional diagnostic methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study developed a Continuous Wavelet Transform-enhanced Transformer framework (CWT-STNet) for early-stage detection of bacterial wilt using hyperspectral data collected from tomato canopies under controlled inoculation conditions. Infection severity was categorized into grades 0 to 4, with levels 1\u20132 defined as the early infection stage. Raw spectral data were fused with multi-scale wavelet features extracted using Continuous Wavelet Transform (CWT), and the effects of different scale combinations on model performance were systematically evaluated. CWT-STNet was compared with one-dimensional convolutional neural networks (1D-CNNs), random forests (RF), and support vector machines (SVM). SHapley Additive exPlanations (SHAP) were further used to interpret model predictions and identify disease-sensitive spectral features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed CWT-STNet framework achieved an overall accuracy of 96.77% across all infection levels and an early-stage detection accuracy of 93.60%. In comparison, models relying only on raw spectra achieved 84.15% accuracy in distinguishing early disease severity. Incorporating CWT-derived multi-scale features improved the performance of all evaluated models, while ablation analysis confirmed the importance of combining raw spectra with multi-scale wavelet features. SHAP analysis identified spectral features around 869.0 nm and the 1160\u20131190 nm region as particularly important for early bacterial wilt detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The identified spectral regions were associated with physiological and structural changes related to bacterial wilt infection, including alterations in plant water status, leaf cellular structure, and pigment-related responses. These findings demonstrate that the proposed CWT-STNet framework provides a robust, interpretable, and non-destructive approach for early detection of tomato bacterial wilt and has potential for precision disease monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1911300","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:45:07Z","timestamp":1788417907000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A continuous wavelet transform-enhanced transformer model for spectroscopic detection of bacterial wilt in tomato at early infection stages"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiaoya","family":"Ru","sequence":"first","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzhi","family":"He","sequence":"additional","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaiheng","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Si","sequence":"additional","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunming","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Optical, Mechanical and Electrical Engineering, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhujun","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Horticulture, Zhejiang Agriculture and Forestry University","place":["Hangzhou, Zhejiang, China"]},{"name":"Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Ji","sequence":"additional","affiliation":[{"name":"Agricultural College, Shihezi University","place":["Shihezi, Xinjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.plantsci.2025.112544","article-title":"A non-destructive and precise root monitoring system for hydroponic crops using SWIR hyperspectral imaging","volume":"357","author":"Antony","year":"2025","journal-title":"Plant Sci."},{"key":"B2","first-page":"227","article-title":"Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition","volume-title":"Neurocomputing. Nato Asi Series","author":"Bridle","year":"1990"},{"key":"B3","doi-asserted-by":"publisher","first-page":"677","DOI":"10.2307\/2657068","article-title":"Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration","volume":"88","author":"Carter","year":"2001","journal-title":"Am. J. Bot."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105708","article-title":"Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data","volume":"177","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"B5","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1016\/j.rse.2009.12.005","article-title":"Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation","volume":"114","author":"Cheng","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2512757123","article-title":"The EPS-I exopolysaccharide transforms Ralstonia wilt pathogen biofilms into viscoelastic fluids for rapid dissemination in planta","volume":"123","author":"Cope-Arguello","year":"2026","journal-title":"PNAS"},{"key":"B7","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture11100998","article-title":"Detection of fusarium head blight in wheat ears using continuous wavelet analysis and pso-svm","volume":"11","author":"Huang","year":"2021","journal-title":"Agriculture"},{"key":"B10","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/0034-4257(95)00238-3","article-title":"Estimating leaf biochemistry using the PROSPECT leaf optical properties model","volume":"56","author":"Jacquemoud","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2025.105826","article-title":"Enhancing hyperspectral image classification through transformer-based contextualization and novel logarithmic convolutional techniques","volume":"147","author":"Kumar","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107448","article-title":"Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification","volume":"203","author":"Liu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1007\/s11119-023-10010-2","article-title":"Combining multiple spectral enhancement features for improving spectroscopic asymptomatic detection and symptomatic severity classification of southern corn leaf blight","volume":"24","author":"Lv","year":"2023","journal-title":"Precis. Agric."},{"key":"B14","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":"B15","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1146\/annurev-phyto-080417-050100","article-title":"Hyperspectral sensors and imaging technologies in phytopathology: State of the art","volume":"56","author":"Mahlein","year":"2018","journal-title":"Annu. Rev. Phytopathol."},{"key":"B16","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1111\/tpj.16101","article-title":"Image-based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato","volume":"113","author":"M\u00e9line","year":"2023","journal-title":"Plant J."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-018-0349-9","article-title":"Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems","volume":"14","author":"Nagasubramanian","year":"2018","journal-title":"Plant Methods"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2024.125001","article-title":"Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram","volume":"324","author":"Ong","year":"2025","journal-title":"Spectrochimica Acta Part. A. Mol. Biomol. Spectrosc."},{"key":"B19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1093\/ismejo\/wraf210","article-title":"Stenotrophomonas maltophilia impedes Bacillus biocontrol of tomato wilt disease by degrading its lipopeptide antibiotics","volume":"19","author":"Peng","year":"2025","journal-title":"ISME J."},{"key":"B20","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1021\/acs.analchem.5c06324","article-title":"Research on spectral baseline reconstruction of broad-spectrum absorption characteristics for alkanes mixture gases based on laser absorption spectroscopy","volume":"98","author":"Peng","year":"2026","journal-title":"Anal. Chem."},{"key":"B21","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.inpa.2025.09.005","article-title":"Precision pest management in agriculture using Inception V3 and EfficientNet B4: A deep learning approach for crop protection","volume":"13","author":"Ray","year":"2026","journal-title":"Inf. Process. Agric."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2316164121","article-title":"Mechanistic links between physiology and spectral reflectance enable previsual detection of oak wilt and drought stress","volume":"121","author":"Sapes","year":"2024","journal-title":"PNAS"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109617","article-title":"Deep learning-based classification of visual symptoms of bacterial wilt disease caused by Ralstonia solanacearum in tomato plants","volume":"227","author":"V\u00e1sconez","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.scienta.2023.112296","article-title":"Biocontrol of tomato bacterial wilt by a combination of Bacillus subtilis GSJB-1210 and ningnanmycin","volume":"321","author":"Wang","year":"2023","journal-title":"Sci. Hortic."},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102353","article-title":"Identification of tomato bacterial wilt severity based on hyperspectral imaging technology and spectrum Transformer network","volume":"78","author":"Wang","year":"2023","journal-title":"Ecol. Inf."},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2026.105110","article-title":"Feature replacement and fusion enhance the accuracy of canopy spectral monitoring models for winter wheat leaf water content","volume":"146","author":"Wang","year":"2026","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108571","article-title":"Hyperspectral imaging detects biological stress of wheat for early diagnosis of crown rot disease","volume":"217","author":"Xie","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-022-00841-x","article-title":"Real-time monitoring of Ralstonia solanacearum infection progress in tomato and Arabidopsis using bioluminescence imaging technology","volume":"18","author":"Xu","year":"2022","journal-title":"Plant Methods"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2024.109176","article-title":"Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains","volume":"306","author":"Xu","year":"2024","journal-title":"Agric. Water Manage."},{"key":"B31","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"B32","doi-asserted-by":"publisher","first-page":"6088","DOI":"10.1038\/s41467-021-26335-3","article-title":"Divergent abiotic spectral pathways unravel pathogen stress signals across species","volume":"12","author":"Zarco-Tejada","year":"2021","journal-title":"Nat. Commun."},{"key":"B33","doi-asserted-by":"publisher","first-page":"7166","DOI":"10.1007\/s10489-021-02223-7","article-title":"Beyond AP: a new evaluation index for multiclass classification task accuracy","volume":"51","author":"Zhang","year":"2021","journal-title":"Appl. Intell."},{"key":"B34","doi-asserted-by":"publisher","first-page":"27666","DOI":"10.1038\/s41598-024-78650-6","article-title":"Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci. Rep."},{"key":"B35","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1111\/pbi.14539","article-title":"Reduced content of gamma-aminobutyric acid enhances resistance to bacterial wilt disease in tomato","volume":"23","author":"Zhao","year":"2025","journal-title":"Plant Bio\/Technol. J."},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.48130\/vegres-0025-0010","article-title":"Early detection of tomato leaf spot and wilt diseases based on hyperspectral imaging technology","volume":"5","author":"Zhou","year":"2025","journal-title":"Vegetable Res."},{"key":"B37","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.eng.2022.10.006","article-title":"Fingerprint spectral signatures revealing the spatiotemporal dynamics of Bipolaris spot blotch progression for presymptomatic diagnosis","volume":"22","author":"Zhu","year":"2023","journal-title":"Engineering"}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1911300\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:45:09Z","timestamp":1788417909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1911300\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":37,"alternative-id":["10.3389\/fpls.2026.1911300"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1911300","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]},"article-number":"1911300"}}