{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:53:47Z","timestamp":1777269227967,"version":"3.51.4"},"reference-count":93,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T00:00:00Z","timestamp":1640304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005401","name":"Ministry of Agricultural, Food and Forestry Policies","doi-asserted-by":"publisher","award":["DM36503.7305.2018"],"award-info":[{"award-number":["DM36503.7305.2018"]}],"id":[{"id":"10.13039\/501100005401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection.<\/jats:p>","DOI":"10.3390\/rs14010084","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8666-2424","authenticated-orcid":false,"given":"Catello","family":"Pane","sequence":"first","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e l\u2019Analisi dell\u2019Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1502-1595","authenticated-orcid":false,"given":"Gelsomina","family":"Manganiello","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e l\u2019Analisi dell\u2019Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Nicastro","sequence":"additional","affiliation":[{"name":"Consiglio per la Ricerca in Agricoltura e l\u2019Analisi dell\u2019Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8343-723X","authenticated-orcid":false,"given":"Francesco","family":"Carotenuto","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze della Terra, dell\u2019Ambiente e delle Risorse, Universit\u00e0 degli Studi di Napoli Federico II, Monte Sant\u2019Angelo, Via Cinthia, 21, 80126 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"300","DOI":"10.3389\/fpls.2017.00300","article-title":"Nutritional, biophysical and physiological characteristics of wild rocket genotypes as affected by soilless cultivation system, salinity level of nutrient solution and growing period","volume":"8","author":"Bonasia","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Caruso, G., Stoleru, V., De Pascale, S., Cozzolino, E., Pannico, A., Giordano, M., Teliban, G., Cuciniello, A., and Rouphael, Y. (2019). Production, leaf quality and antioxidants of perennial wall rocket as affected by crop cycle and mulching type. Agronomy, 9.","DOI":"10.3390\/agronomy9040194"},{"key":"ref_3","unstructured":"Freshplaza (2021, June 12). Il Valore del Settore della Rucola in Italia e\u2019 Stimato in 30\u201340 Milioni di Euro Solo per L\u2019export 2012. Available online: https:\/\/www.freshplaza.it\/article\/4044942\/il-valore-del-settore-della-rucola-in-italia-e-stimato-in-30-40-milioni-di-euro-solo-per-l-export\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Caruso, G., Parrella, G., Giorgini, M., and Nicoletti, R. (2018). Crop systems, quality and protection of Diplotaxis tenuifolia. Agriculture, 8.","DOI":"10.3390\/agriculture8040055"},{"key":"ref_5","first-page":"36","article-title":"Diplotaxis tenuifolia: Biology, production and properties","volume":"1","author":"Nicoletti","year":"2007","journal-title":"Eur. J. Plant Sci. Biotechnol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s00374-015-1038-8","article-title":"Declined soil suppressiveness to Fusarium oxysporum by rhizosphere microflora of cotton in soil sickness","volume":"51","author":"Li","year":"2015","journal-title":"Biol. Fertil. Soils"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1094\/PDIS.2003.87.2.201A","article-title":"First of Fusarium oxysporum on Eruca vesicaria and Diplotaxis spp. in Europe","volume":"87","author":"Garibaldi","year":"2003","journal-title":"Plant Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02981311","article-title":"Evidence for an expanded host range of Fusarium oxysporum f. sp. raphani","volume":"34","author":"Garibaldi","year":"2006","journal-title":"Phytoparasitica"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/j.1439-0434.2006.01199.x","article-title":"Analysis of vegetative compatibility groups of Fusarium oxysporum from Eruca vesicaria and Diplotaxis tenuifolia","volume":"155","author":"Catti","year":"2007","journal-title":"J. Phytopathol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1094\/PDIS-12-18-2143-PDN","article-title":"First Report of Fusarium oxysporum and Fusarium redolens causing wilting and yellowing of wild rocket (Diplotaxis tenuifolia) in the United Kingdom","volume":"103","author":"Taylor","year":"2019","journal-title":"Plant Dis."},{"key":"ref_11","first-page":"345","article-title":"Seed transmission of Fusarium oxysporum of Eruca vesicaria and Diplotaxis muralis","volume":"111","author":"Garibaldi","year":"2004","journal-title":"J. Plant Dis. Prot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Martos, V., Ahmad, A., Cartujo, P., and Ordo\u00f1ez, J. (2021). Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Appl. Sci., 11.","DOI":"10.3390\/app11135911"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"27790","DOI":"10.1038\/srep27790","article-title":"Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/jxb\/erl123","article-title":"Hyperspectral remote sensing of plant pigments","volume":"58","author":"Blackburn","year":"2007","journal-title":"J. Experiment. Bot."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41348-017-0124-6","article-title":"Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective","volume":"125","author":"Thomas","year":"2018","journal-title":"J. Plant Dis. Prot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106453","DOI":"10.1016\/j.compag.2021.106453","article-title":"Detection of leek white tip disease under field conditions using hyperspectral proximal sensing and supervised machine learning","volume":"190","author":"Appeltans","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3390\/agriculture4010032","article-title":"Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on Wheat","volume":"4","author":"Bauriegel","year":"2014","journal-title":"Agriculture"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.postharvbio.2018.05.004","article-title":"Non-destructive recognition and classification of citrus fruit blemishes based on ant colony optimized spectral information","volume":"143","author":"Zhang","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1016\/j.tplants.2016.10.002","article-title":"Machine learning for plant phenotyping needs image processing","volume":"21","author":"Tsaftaris","year":"2016","journal-title":"Trends Plant Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1038\/s41598-017-04501-2","article-title":"Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers","volume":"7","author":"Zhu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liang, J., Li, X., Zhu, P., Xu, N., and He, Y. (2019). Hyperspectral reflectance imaging combined with multivariate analysis for diagnosis of Sclerotinia stem rot on Arabidopsis thaliana leaves. Appl. Sci., 9.","DOI":"10.3390\/app9102092"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112570","DOI":"10.1016\/j.rse.2021.112570","article-title":"Modelling hyperspectral- and thermal-based plant traits for the early detection of Phytophthora-induced symptoms in oak decline","volume":"263","author":"Hornero","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., and Kwasniewski, M.T. (2021). Early detection of plant viral disease using hyperspectral imaging and deep learning. Sensors, 21.","DOI":"10.3390\/s21030742"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei, X., Johnson, M.A., Langston, D.B., Mehl, H.L., and Li, S. (2021). Identifying optimal wavelengths as disease signatures using hyperspectral sensor and machine learning. Remote Sens., 13.","DOI":"10.3390\/rs13142833"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1094\/PHYTO-96-0068","article-title":"Effects of different 3-year cropping systems on soil microbial communities and Rhizoctonia diseases of potato","volume":"96","author":"Larkin","year":"2006","journal-title":"Phytopathology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/aab.12362","article-title":"Discussion on disease severity index values. Part I: Warning on inherent errors and suggestions to maximize accuracy","volume":"171","author":"Chiang","year":"2017","journal-title":"Ann. Appl. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Manganiello, G., Nicastro, N., Caputo, M., Zaccardelli, M., Cardi, T., and Pane, C. (2021). Functional hyperspectral imaging by high-related vegetation indices to track the wide-spectrum Trichoderma biocontrol activity against soil-borne diseases of baby-leaf vegetables. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.630059"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijfoodmicro.2011.12.022","article-title":"Quantification of Fusarium graminearum and Fusarium culmorum by real-time PCR system and zearalenone assessment in maize","volume":"15","author":"Atoui","year":"2012","journal-title":"Int. J. Food Microbiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.jplph.2010.09.009","article-title":"Soluble silicon modulates expression of Arabidopsis thaliana genes involved in copper stress","volume":"168","author":"Khandekar","year":"2011","journal-title":"J. Plant Physiol."},{"key":"ref_33","first-page":"1","article-title":"Package \u2018raster\u2019","volume":"734","author":"Hijmans","year":"2015","journal-title":"R Package"},{"key":"ref_34","unstructured":"Kassambara, A., and Mundt, F. (2021, June 16). Package \u2018Factoextra\u2019. Extract and Visualize the Results of Multivariate Data Analyses 76. Available online: https:\/\/cloud.r-project.org\/package=factoextra."},{"key":"ref_35","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s13007-018-0313-8","article-title":"Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform","volume":"14","author":"Thomas","year":"2018","journal-title":"Plant Methods"},{"key":"ref_39","unstructured":"Jensen, J.R. (2006). Remote Sensing of the Environment: An Earth Resource Perspective, Pearson Education. [2nd ed.]."},{"key":"ref_40","first-page":"1","article-title":"Foliar disease detection in the field using optical sensor fusion","volume":"6","author":"Bravo","year":"2004","journal-title":"Agric. Eng. Int. CIGR J. Sci. Res. Dev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11119-011-9237-2","article-title":"Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet","volume":"13","author":"Mahlein","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_42","unstructured":"Wahabzada, M., Kersting, K., Bauckhage, C., R\u00f6mer, C., Ballvora, A., Pinto, F., Rascher, U., L\u00e9on, J., and Pl\u00fcmer, L. (2012, January 15\u201317). Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants. Proceedings of the 28th Conference, Avalon, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1071\/FP16127","article-title":"Observation of plant\u2013pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements","volume":"44","author":"Thomas","year":"2017","journal-title":"Funct. Plant Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-015-0073-7","article-title":"Hyperspectral phenotyping on the microscopic scale: Towards automated characterization of plant-pathogen interactions","volume":"11","author":"Kuska","year":"2015","journal-title":"Plant Methods"},{"key":"ref_45","first-page":"1","article-title":"Improvement of lesion phenotyping in Cercospora beticola-sugar beet interaction by hyperspectral imaging","volume":"1","author":"Leucker","year":"2016","journal-title":"Phytopathology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.fcr.2011.02.007","article-title":"Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields","volume":"122","author":"Mahlein","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.snb.2018.06.121","article-title":"Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging","volume":"273","author":"Strajnar","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7722","DOI":"10.1038\/srep07722","article-title":"Water balance altered in cucumber plants infected with Fusarium oxysporum f. sp. cucumerinum","volume":"5","author":"Wang","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1134\/S1021443706010031","article-title":"Changes in the photosynthetic apparatus during Fusarium wilt of tomato","volume":"53","author":"Pshibytko","year":"2006","journal-title":"Russ. J. Plant Physiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1046\/j.1469-8137.1999.00365.x","article-title":"Process of tomato root colonization by a pathogenic strain of Fusarium oxysporum f. sp. lycopersici in comparison with a non-pathogenic strain","volume":"141","author":"Olivain","year":"1999","journal-title":"New Phytol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1094\/PHYTO.2001.91.8.730","article-title":"Cytological analysis of defense related mechanisms induced in pea root tissues in response to colonization by nonpathogenic Fusarium oxysporum Fo47","volume":"91","author":"Benhamou","year":"2001","journal-title":"Phytopathology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3389\/fpls.2016.00031","article-title":"Changes in the proteome of xylem sap in Brassica oleracea in response to Fusarium oxysporum Stress","volume":"7","author":"Pu","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1094\/MPMI.2002.15.2.172","article-title":"Novel aspects of tomato root colonization and infection by Fusarium oxysporum f. sp. radicis-lycopersici revealed by confocal laser scanning microscopic analysis using the green fluorescent protein as a marker","volume":"15","author":"Lagopodi","year":"2002","journal-title":"Mol. Plant Microbe Interact."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1006\/pmpp.1995.1003","article-title":"The rate of vascular colonization as a measure of the genotypic interaction between various cultivars of tomato and various formae or races of Fusarium oxysporum","volume":"46","author":"Gao","year":"1995","journal-title":"Physiol. Mol. Plant Pat."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/BF00041269","article-title":"Localization ability, latent period and wilting rate in eleven carnation cultivars with partial resistance to Fusarium wilt","volume":"59","author":"Baayen","year":"1992","journal-title":"Euphytica"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1017\/S0953756200002495","article-title":"Cytology and ultrastructure of the infection of wheat spikes by Fusarium culmorum","volume":"104","author":"Kang","year":"2000","journal-title":"Mycol. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.compag.2010.12.006","article-title":"Early detection of Fusarium infection in wheat using hyper-spectral imaging","volume":"75","author":"Bauriegel","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/S1537-5110(03)00090-4","article-title":"Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat","volume":"86","author":"Muhammed","year":"2003","journal-title":"Biosyst. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chitarra, W., Siciliano, I., Ferrocino, I., Gullino, M.L., and Garibaldi, A. (2015). Effect of elevated atmospheric CO2 and temperature on the disease severity of rocket plants caused by Fusarium Wilt under phytotron conditions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140769"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s11274-013-1564-1","article-title":"Fusaric acid accelerates the senescence of leaf in banana when infected by Fusarium","volume":"30","author":"Dong","year":"2014","journal-title":"World J. Microbiol. Biotechnol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10658-013-0306-4","article-title":"Effect of fusaric acid on the leaf physiology of cucumber seedlings","volume":"138","author":"Wang","year":"2014","journal-title":"Eur. J. Plant. Pathol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"702842","DOI":"10.3389\/fpls.2021.702842","article-title":"Physiological response of cape gooseberry plants to Fusarium oxysporum f. sp. physali, fusaric acid, and water deficit in a hydrophonic system","volume":"12","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.plaphy.2017.06.028","article-title":"Role of fusaric acid in the development of \u2018Fusarium wilt\u2019 symptoms in tomato: Physiological, biochemical and proteomic perspectives","volume":"118","author":"Singh","year":"2017","journal-title":"Plant Physiol. Biochem."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1093\/aob\/mcx065","article-title":"Wilted cucumber plants infected by Fusarium oxysporum f. sp. cucumerinum do not suffer from water shortage","volume":"120","author":"Sun","year":"2017","journal-title":"Ann. Bot."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part I: Theoretical approach","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0034-4257(02)00113-X","article-title":"Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects","volume":"84","author":"Pushnik","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_67","first-page":"244","article-title":"Detection of significant wavelengths for identifying and classifying during the incubation period and water stress in plants using reflectance spectroscopy","volume":"59","year":"2019","journal-title":"J. Plant Prot. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(94)90079-5","article-title":"Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands","volume":"50","author":"Carter","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1017\/S0014479717000229","article-title":"Assessing nitrogen nutritional status, biomass and yield of cotton with NDVI, SPAD and petiole sap nitrate concentration","volume":"54","author":"Zhou","year":"2018","journal-title":"Exp. Agric."},{"key":"ref_70","unstructured":"Danson, F.M., and Plummer, S.E. (1995). Advances in Crop Monitoring by Remote Sensing. Advances in Environmental Remote Sensing, John and Wiley and Sons."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1071\/AR9950113","article-title":"Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite","volume":"46","author":"Smith","year":"1995","journal-title":"Aus. J. Agric. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/S0176-1617(96)80285-9","article-title":"Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.ecoleng.2003.10.004","article-title":"Hyperspectral reflectance of emergent macrophytes as an indicator of water column ammonia in an oligohaline, subtropical marsh","volume":"21","author":"Tilley","year":"2003","journal-title":"Ecol. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1094\/PHYTO.2003.93.6.720","article-title":"Remote detection of rhizomania in sugar beets","volume":"93","author":"Steddom","year":"2003","journal-title":"Phytopathology"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2012.05.003","article-title":"Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices","volume":"71","author":"Stagakis","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1094\/PDIS-10-17-1536-RE","article-title":"The use of features from fluorescence, thermography, and NDVI imaging to detect biotic stress in lettuce","volume":"102","author":"Sandmann","year":"2018","journal-title":"Plant Dis."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"104885","DOI":"10.1016\/j.cropro.2019.104885","article-title":"High-throughput field phenotyping of Ascochyta blight disease severity in chickpea","volume":"125","author":"Zhang","year":"2019","journal-title":"Crop Prot."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yang, C., Guo, W., Zhang, L., and Zhang, D. (2020). Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sens., 12.","DOI":"10.3390\/rs12121930"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Liu, L., Dong, Y., Huang, W., Du, X., and Ma, H. (2020). Monitoring wheat Fusarium head blight using unmanned aerial vehicle hyperspectral imagery. Remote Sens., 12.","DOI":"10.3390\/rs12223811"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1016\/j.rse.2011.04.036","article-title":"Assessing structural effects on PRI for stress detection in conifer forests","volume":"115","author":"Morales","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Mahlein, A.K., Alisaac, E., Al Masri, A., Behmann, J., Dehne, H.W., and Oerke, E.C. (2019). Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring Fusarium head blight of wheat on spikelet scale. Sensors, 19.","DOI":"10.3390\/s19102281"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"105665","DOI":"10.1016\/j.compag.2020.105665","article-title":"Assessing winter wheat foliage disease severity using aerial imagery acquired from small unmanned aerial vehicle (UAV)","volume":"176","author":"Bhandari","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/TGRS.2020.3000992","article-title":"Monitoring of wheat powdery mildew disease severity using multiangle hyperspectral remote sensing","volume":"59","author":"He","year":"2020","journal-title":"Trans. Geosci. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Pane, C., Manganiello, G., Nicastro, N., Cardi, T., and Carotenuto, F. (2021). Powdery mildew caused by Erysiphe cruciferarum on wild rocket (Diplotaxis tenuifolia): Hyperspectral imaging and machine learning modeling for non-destructive disease detection. Agriculture, 11.","DOI":"10.3390\/agriculture11040337"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1093\/treephys\/7.1-2-3-4.33","article-title":"Exploring the relationship between reflectance red edge and chlorophyll content in slash pine","volume":"7","author":"Curran","year":"1990","journal-title":"Tree Physiol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"619987","DOI":"10.3389\/fpls.2021.619987","article-title":"Photosynthetic physiology of blue, green, and red light: Light intensity effects and underlying mechanisms","volume":"12","author":"Liu","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/S0176-1617(96)80283-5","article-title":"Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements","volume":"148","author":"Lichtenthaler","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s11120-005-0413-y","article-title":"Photochemical reactions of chlorophyll in dehydrated Photosystem II: Two chlorophyll forms (680 and 700 nm)","volume":"84","author":"Heber","year":"2005","journal-title":"Photosynth. Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s11104-011-0733-y","article-title":"Effects of cadmium and salicylic acid on growth, spectral reflectance and photosynthesis of castor bean seedlings","volume":"344","author":"Liu","year":"2011","journal-title":"Plant Soil"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"337","DOI":"10.32615\/ps.2021.029","article-title":"Ethylene-dependent effects of fusaric acid on the photosynthetic activity of tomato plants","volume":"59","author":"Iqbal","year":"2021","journal-title":"Photosynthetica"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"301","DOI":"10.17584\/rcch.2020v14i3.10938","article-title":"Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for vascular wilt detection","volume":"14","author":"Fischer","year":"2020","journal-title":"Rev. Colomb. Cienc. Hort\u00edc."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., and Jin, Y. (2020). Recognition of Banana Fusarium Wilt based on UAV remote sensing. Remote Sens., 12.","DOI":"10.3390\/rs12060938"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, X., and Wu, W. (2020). A Hyperspectral Bidirectional Reflectance Model for Land Surface. Sensors, 20.","DOI":"10.3390\/s20164456"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/84\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:53:10Z","timestamp":1760169190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/84"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,24]]},"references-count":93,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14010084"],"URL":"https:\/\/doi.org\/10.3390\/rs14010084","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,24]]}}}