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Plant Sci."],"abstract":"<jats:p>Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by <jats:italic>Pseudomonas syringae<\/jats:italic> pv. <jats:italic>tomato<\/jats:italic> (Pst), and bacterial spot, caused by <jats:italic>Xanthomonas euvesicatoria<\/jats:italic> (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine \u2013 SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants\u2019 defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired <jats:italic>in-vivo<\/jats:italic> for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.<\/jats:p>","DOI":"10.3389\/fpls.2023.1242201","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T13:26:12Z","timestamp":1692278772000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling"],"prefix":"10.3389","volume":"14","author":[{"given":"Mafalda","family":"Reis Pereira","sequence":"first","affiliation":[]},{"given":"Filipe Neves dos","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"Tavares","sequence":"additional","affiliation":[]},{"given":"M\u00e1rio","family":"Cunha","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1094\/PDIS-12-16-1699-RE","article-title":"Early detection of ganoderma basal stem rot of oil palms using artificial neural network spectral analysis","volume":"101","author":"Ahmadi","year":"2017","journal-title":"Plant Dis."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0037836","article-title":"Evolutionary and experimental assessment of novel markers for detection of Xanthomonas euvesicatoria in plant samples","volume":"7","author":"Albuquerque","year":"2012","journal-title":"PloS One"},{"key":"B3","doi-asserted-by":"publisher","first-page":"323","DOI":"10.3390\/horticulturae9030323","article-title":"Effects of exogenously applied copper in tomato plants\u2019 Oxidative and nitrogen metabolisms under organic farming conditions","volume":"9","author":"Alves","year":"2023","journal-title":"Horticulturae"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1109\/ICECCO.2014.6997585","article-title":"Matplotlib in python","author":"Ari","year":"2014"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/22797254.2017.1391054","article-title":"Detection of Fire Blight disease in pear trees by hyperspectral data","volume":"51","author":"Bagheri","year":"2018","journal-title":"Eur. 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