{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T15:42:48Z","timestamp":1774712568256,"version":"3.50.1"},"reference-count":98,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agronomy"],"abstract":"<jats:p>Early and accurate disease diagnosis is pivotal for effective phytosanitary management strategies in agriculture. Hyperspectral sensing has emerged as a promising tool for early disease detection, yet challenges remain in effectively harnessing its potential. This study compares parametric spectral Vegetation Indices (VIs) and a nonparametric Gaussian Process Classification based on an Automated Spectral Band Analysis Tool (GPC-BAT) for diagnosing plant bacterial diseases using hyperspectral data. The study conducted experiments on tomato plants in controlled conditions and kiwi plants in field settings to assess the performance of VIs and GPC-BAT. In the tomato experiment, the modeling processes were applied to classify the spectral data measured on the healthy class of plants (sprayed with water only) and discriminate them from the data captured on plants inoculated with the two bacterial suspensions (108 CFU mL\u22121). In the kiwi experiment, the standard modeling results of the spectral data collected on nonsymptomatic plants were compared to the ones obtained using symptomatic plants\u2019 spectral data. VIs, known for their simplicity in extracting biophysical information, successfully distinguished healthy and diseased tissues in both plant species. The overall accuracy achieved was 63% and 71% for tomato and kiwi, respectively. Limitations were observed, particularly in differentiating specific disease infections accurately. On the other hand, GPC-BAT, after feature reduction, showcased enhanced accuracy in identifying healthy and diseased tissues. The overall accuracy ranged from 70% to 75% in the tomato and kiwi case studies. Despite its effectiveness, the model faced challenges in accurately predicting certain disease infections, especially in the early stages. Comparative analysis revealed commonalities and differences in the spectral bands identified by both approaches, with overlaps in critical regions across plant species. Notably, these spectral regions corresponded to the absorption regions of various photosynthetic pigments and structural components affected by bacterial infections in plant leaves. The study underscores the potential of hyperspectral sensing in disease diagnosis and highlights the strengths and limitations of VIs and GPC-BAT. The identified spectral features hold biological significance, suggesting correlations between bacterial infections and alterations in plant pigments and structural components. Future research avenues could focus on refining these approaches for improved accuracy in diagnosing diverse plant\u2013pathogen interactions, thereby aiding disease diagnosis. Specifically, efforts could be directed towards adapting these methodologies for early detection, even before symptom manifestation, to better manage agricultural diseases.<\/jats:p>","DOI":"10.3390\/agronomy14030493","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T09:26:17Z","timestamp":1709112377000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches"],"prefix":"10.3390","volume":"14","author":[{"given":"Mafalda","family":"Reis Pereira","sequence":"first","affiliation":[{"name":"Faculty of Sciences, University of Porto (FCUP), Rua do Campo Alegre, 4169-007 Porto, Portugal"},{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, 46980 Paterna, Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7017-9323","authenticated-orcid":false,"given":"Renan","family":"Tosin","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Porto (FCUP), Rua do Campo Alegre, 4169-007 Porto, Portugal"},{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3188-1448","authenticated-orcid":false,"given":"Juan Pablo","family":"Rivera Caicedo","sequence":"additional","affiliation":[{"name":"CONACYT-UAN, Secretary of Research and Postgraduate, Autonomous University of Nayarit, Tepic C.P., Nayarit 63155, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9913-1155","authenticated-orcid":false,"given":"Fernando","family":"Tavares","sequence":"additional","affiliation":[{"name":"BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vair\u00e3o, 4485-661 Vair\u00e3o, Portugal"},{"name":"CIBIO, Research Centre in Biodiversity and Genetic Resources, InBIO Associate Laboratory, Campus de Vair\u00e3o, Universidade do Porto, 4485-661 Vair\u00e3o, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe","family":"Neves dos Santos","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8299-324X","authenticated-orcid":false,"given":"M\u00e1rio","family":"Cunha","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Porto (FCUP), Rua do Campo Alegre, 4169-007 Porto, Portugal"},{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2022239118","DOI":"10.1073\/pnas.2022239118","article-title":"The persistent threat of emerging plant disease pandemics to global food security","volume":"118","author":"Ristaino","year":"2021","journal-title":"Proc. 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