{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T04:28:02Z","timestamp":1787372882651,"version":"build-2736575974"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32172653"],"award-info":[{"award-number":["32172653"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32201252"],"award-info":[{"award-number":["32201252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["32172653"],"award-info":[{"award-number":["32172653"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["32201252"],"award-info":[{"award-number":["32201252"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005196","name":"Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences","doi-asserted-by":"publisher","award":["CAAS-ASTIP-IVFCAAS"],"award-info":[{"award-number":["CAAS-ASTIP-IVFCAAS"]}],"id":[{"id":"10.13039\/501100005196","id-type":"DOI","asserted-by":"publisher"}]},{"award":["CAAS-ASTIP-IVFCAAS"],"award-info":[{"award-number":["CAAS-ASTIP-IVFCAAS"]}],"id":[{"id":"https:\/\/ror.org\/0313jb750","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2023YFD1401200"],"award-info":[{"award-number":["2023YFD1401200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"China Agriculture Research System of MOF and MARA","award":["CARS-23"],"award-info":[{"award-number":["CARS-23"]}]},{"award":["CARS-23"],"award-info":[{"award-number":["CARS-23"]}],"id":[{"id":"https:\/\/ror.org\/03p5eky76","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Airborne bacterial diseases driven by pathogen aerosols in enclosed greenhouses spread rapidly, challenging traditional early-warning methods. This study developed a two-step monitoring system for cucumber angular leaf spot using low-cost particulate matter (PM) sensors, qPCR, and machine learning. Evaluated across spatially independent greenhouse trials using 732 plot-days of data, PM sensors were utilized as dynamic physical proxies alongside microclimate data. These proxies continuously estimated the fluctuations of pathogen aerosols suspended in the greenhouse air. When estimated aerosol risks exceeded a pathogenic threshold, targeted air sampling and qPCR quantification were triggered. For pathogen monitoring, the Extra Trees (ET) surveillance model accurately predicted the accumulation of airborne pathogen aerosols (R2 = 0.884). For disease forecasting, by integrating the quantified aerosol loads with environmental factors, the XGBoost prediction model forecasted the daily disease index change rate with high precision (R2 = 0.874). SHapley Additive exPlanations (SHAP) analysis confirmed that the concentration of airborne pathogen aerosols and vapor pressure deficit were primary drivers of disease expansion. By combining continuous physical sensing of greenhouse air with risk-triggered biological quantification, this framework provides a feasible strategy to partly compensate for the lack of biological specificity of PM sensors and supports early-warning management of airborne bacterial diseases in protected agriculture.<\/jats:p>","DOI":"10.3390\/plants15162510","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T08:10:20Z","timestamp":1787213420000},"page":"2510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early\u00a0Warning of Cucumber Angular Leaf Spot by Estimating Airborne Pathogen Aerosols with Particulate Matter Sensors"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2349-0191","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"College of Plant Protection, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0190-0919","authenticated-orcid":false,"given":"Leng","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuheng","family":"Xing","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanxia","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewen","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengfei","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Xiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianhua","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7122-0340","authenticated-orcid":false,"given":"Baoju","family":"Li","sequence":"additional","affiliation":[{"name":"College of Plant Protection, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1208-8463","authenticated-orcid":false,"given":"Ali","family":"Chai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106436","DOI":"10.1016\/j.buildenv.2019.106436","article-title":"Assessment of Bioaerosols in Indoor Air of Glasshouses Located in a Botanical Garden","volume":"166","author":"Ropek","year":"2019","journal-title":"Build. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yusuf, A.G., Al-Yahya, F.A., Saleh, A.A., and Abdel-Ghany, A.M. (2025). Optimizing Greenhouse Microclimate for Plant Pathology: Challenges and Cooling Solutions for Pathogen Control in Arid Regions. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1492760"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yuan, L., Jiang, H., Li, T., Liu, Q., Jiang, X., Han, X., Wei, Y., Yin, X., and Wang, S. (2024). A Simulation Study to Reveal the Epidemiology and Aerosol Transmission Characteristics of Botrytis cinerea in Grape Greenhouses. Pathogens, 13.","DOI":"10.3390\/pathogens13060505"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Shi, Y., Wang, Y., Xie, X., Li, L., Guo, L., Chai, A., and Li, B. (2021). Quantifying Airborne Dispersal Route of Corynespora cassiicola in Greenhouses. Front. Microbiol., 12.","DOI":"10.3389\/fmicb.2021.716758"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2501500","DOI":"10.1002\/advs.202501500","article-title":"Diurnal Release of Airborne Pathogen Spores in Greenhouses via the Synergistic Effects of Relative Humidity and Wind","volume":"12","author":"Ma","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5134","DOI":"10.1128\/JCM.41.11.5134-5146.2003","article-title":"Duplex real-time SYBR green PCR assays for detection of 17 species of food- or waterborne pathogens in stools","volume":"41","author":"Fukushima","year":"2003","journal-title":"J. Clin. Microbiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1111\/j.1365-2672.1991.tb03822.x","article-title":"The Dispersal of Bacteria from Leaf Surfaces by Water Splash","volume":"71","author":"Butterworth","year":"1991","journal-title":"J. Appl. Bacteriol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10343-025-01247-0","article-title":"Advancements in Automated Plant Disease Detection: A Comprehensive Review of Imaging Technologies and Deep Learning Applications","volume":"77","author":"Raju","year":"2025","journal-title":"J. Crop Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1094\/PDIS-07-24-1523-RE","article-title":"Early Detection and Quantification of Fusarium Wilt in Greenhouse-Grown Tomato Plants Using Water-Relation Measurements","volume":"109","author":"Friedman","year":"2025","journal-title":"Plant Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1094\/PDIS-91-6-0769A","article-title":"Angular Leaf Spot of Cucumber Caused by Pseudomonas syringae Pv. lachrymans in Kurdistan","volume":"91","author":"Harighi","year":"2007","journal-title":"Plant Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1094\/PDIS-11-15-1332-RE","article-title":"Angular Leaf Spot of Cucurbits Is Associated With Genetically Diverse Pseudomonas Syringae Strains","volume":"100","author":"Newberry","year":"2016","journal-title":"Plant Dis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1111\/mpp.13125","article-title":"A Centenary for Bacterial Spot of Tomato and Pepper","volume":"22","author":"Osdaghi","year":"2021","journal-title":"Mol. Plant Pathol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"141433","DOI":"10.1016\/j.scitotenv.2020.141433","article-title":"Aerosol Transmission of Pseudomonas amygdali pv. lachrymans in Greenhouses","volume":"748","author":"Chai","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chai, A., Yuan, L., Li, X., Li, L., Shi, Y., Xie, X., and Li, B. (2023). Effect of Temperature and Humidity on Dynamics and Transmission of Pseudomonas amygdali pv. lachrymans Aerosols. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1087496"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1128\/aem.56.11.3463-3467.1990","article-title":"Survival of Bacteria during Aerosolization","volume":"56","author":"Marthi","year":"1990","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alsved, M., Holm, S., Christiansen, S., Smidt, M., Rosati, B., Ling, M., Boesen, T., Finster, K., Bilde, M., and L\u00f6ndahl, J. (2019). Corrigendum: Effect of Aerosolization and Drying on the Viability of Pseudomonas syringae Cells. Front. Microbiol., 10.","DOI":"10.3389\/fmicb.2019.02378"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1111\/j.1365-2672.2012.05269.x","article-title":"Distribution and Decline of Human Pathogenic Bacteria in Soil after Application in Irrigation Water and the Potential for Soil-Splash-Mediated Dispersal onto Fresh Produce","volume":"112","author":"Monaghan","year":"2012","journal-title":"J. Appl. Microbiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20141092","DOI":"10.1098\/rsif.2014.1092","article-title":"Fluid Fragmentation Shapes Rain-Induced Foliar Disease Transmission","volume":"12","author":"Gilet","year":"2015","journal-title":"J. R. Soc. Interface"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1094\/PHYTO-02-21-0050-SC","article-title":"Guttation Fluids Containing Pseudomonas amygdali pv. lachrymans Are a Potential Source of Secondary Infection in Cucumber","volume":"111","author":"Zhang","year":"2021","journal-title":"Phytopathology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"56683","DOI":"10.1109\/ACCESS.2021.3069646","article-title":"Plant Disease Detection and Classification by Deep Learning\u2014A Review","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nyawose, T., Maswanganyi, R.C., and Khumalo, P. (2025). A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches. J. Imaging, 11.","DOI":"10.3390\/jimaging11100326"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1186\/s13007-019-0475-z","article-title":"AI-Powered Banana Diseases and Pest Detection","volume":"15","author":"Selvaraj","year":"2019","journal-title":"Plant Methods"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s13007-025-01450-0","article-title":"Recent Advances in Plant Disease Detection: Challenges and Opportunities","volume":"21","author":"Shafay","year":"2025","journal-title":"Plant Methods"},{"key":"ref_24","first-page":"206","article-title":"Inversion of Plant Functional Traits from Hyperspectral Imagery Enhances the Distinction of Wheat Stripe Rust Severity","volume":"16","author":"Ren","year":"2026","journal-title":"Artif. Intell. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.tplants.2022.12.011","article-title":"Modeling Plant Diseases under Climate Change: Evolutionary Perspectives","volume":"28","author":"Yang","year":"2023","journal-title":"Trends Plant Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fenu, G., and Malloci, F.M. (2021). Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5010002"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"110429","DOI":"10.1016\/j.compag.2025.110429","article-title":"The Application of Integrated Deep Learning Models with the Assistance of Meteorological Factors in Forecasting Major Tobacco Diseases","volume":"236","author":"Chen","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105612","DOI":"10.1016\/j.compag.2020.105612","article-title":"Occurrence Prediction of Cotton Pests and Diseases by Bidirectional Long Short-Term Memory Networks with Climate and Atmosphere Circulation","volume":"176","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"34767","DOI":"10.1038\/s41598-025-18613-7","article-title":"Predicting Crop Disease Severity Using Real Time Weather Variability through Machine Learning Algorithms","volume":"15","author":"Bijlwan","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1002\/ps.7733","article-title":"Next-generation Methods for Early Disease Detection in Crops","volume":"80","author":"Trippa","year":"2024","journal-title":"Pest Manag. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1094\/PHYTO-10-22-0362-KD","article-title":"Plant Disease Models and Forecasting: Changes in Principles and Applications over the Last 50 Years","volume":"113","author":"Caffi","year":"2023","journal-title":"Phytopathology"},{"key":"ref_32","first-page":"100089","article-title":"Plant Disease Epidemiology in the Age of Artificial Intelligence and Machine Learning","volume":"3","author":"Neik","year":"2025","journal-title":"Agric. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109905","DOI":"10.1016\/j.compag.2025.109905","article-title":"A Decision Support System for Wheat Powdery Mildew Risk Prediction Using Weather Monitoring, Machine Learning and Explainable Artificial Intelligence","volume":"230","author":"Diachenko","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"110400","DOI":"10.1016\/j.compag.2025.110400","article-title":"Proximal Hyperspectral Imaging for Early Detection and Disease Development Prediction of Septoria Leaf Blotch in Wheat Using Spectral\u2013Temporal Features","volume":"235","author":"Niu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109836","DOI":"10.1016\/j.compag.2024.109836","article-title":"Early Prediction of Cassava Mosaic Disease Onset Based on Remote Sensing and Climatic Data","volume":"230","author":"Chaiyana","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1094\/PHYTO-10-15-0275-R","article-title":"A Physically Based Theoretical Model of Spore Deposition for Predicting Spread of Plant Diseases","volume":"106","author":"Isard","year":"2016","journal-title":"Phytopathology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.21273\/HORTSCI.47.8.1068","article-title":"Factors Affecting Airborne Concentrations of Podosphaera xanthii Conidia and Severity of Gerbera Powdery Mildew","volume":"47","author":"Granke","year":"2012","journal-title":"HortScience"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128122","DOI":"10.1016\/j.envpol.2026.128122","article-title":"Mechanistic insights into humidity-triggered bioaerosol emissions in greenhouses: Source dynamics and physics-based modeling","volume":"398","author":"Li","year":"2026","journal-title":"Environ. Pollut."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/nrmicro1596","article-title":"The disease triangle: Pathogens, the environment and society","volume":"5","author":"Scholthof","year":"2007","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_42","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1146\/annurev.py.30.090192.003005","article-title":"Modeling leaf wetness in relation to plant disease epidemiology","volume":"30","author":"Huber","year":"1992","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1590\/S0103-90162008000700013","article-title":"Obtaining weather data for input to crop disease-warning systems: Leaf wetness duration as a case study","volume":"65","author":"Gleason","year":"2008","journal-title":"Sci. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.agrformet.2009.01.002","article-title":"Airflow and microclimate patterns in a one-hectare Canary type greenhouse: An experimental and CFD assisted study","volume":"149","author":"Majdoubi","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5194\/amt-5-37-2012","article-title":"Autofluorescence of atmospheric bioaerosols: Fluorescent biomolecules and potential interferences","volume":"5","author":"Huffman","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1080\/02786826.2019.1664724","article-title":"Real-time sensing of bioaerosols: Review and current perspectives","volume":"54","author":"Huffman","year":"2020","journal-title":"Aerosol Sci. Technol."}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/16\/2510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T04:14:59Z","timestamp":1787372099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/16\/2510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,20]]},"references-count":48,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["plants15162510"],"URL":"https:\/\/doi.org\/10.3390\/plants15162510","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,20]]}}}