Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/3/493 |
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| author | Mafalda Reis Pereira Jochem Verrelst Renan Tosin Juan Pablo Rivera Caicedo Fernando Tavares Filipe Neves dos Santos Mário Cunha |
| author_facet | Mafalda Reis Pereira Jochem Verrelst Renan Tosin Juan Pablo Rivera Caicedo Fernando Tavares Filipe Neves dos Santos Mário Cunha |
| author_sort | Mafalda Reis Pereira |
| collection | DOAJ |
| description | 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 (10<sup>8</sup> CFU mL<sup>−1</sup>). 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’ 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–pathogen 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. |
| first_indexed | 2024-04-24T18:38:55Z |
| format | Article |
| id | doaj.art-8445b86576474d678955472c44c72c33 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-04-24T18:38:55Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-8445b86576474d678955472c44c72c332024-03-27T13:16:42ZengMDPI AGAgronomy2073-43952024-02-0114349310.3390/agronomy14030493Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification ApproachesMafalda Reis Pereira0Jochem Verrelst1Renan Tosin2Juan Pablo Rivera Caicedo3Fernando Tavares4Filipe Neves dos Santos5Mário Cunha6Faculty of Sciences, University of Porto (FCUP), Rua do Campo Alegre, 4169-007 Porto, PortugalImage Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980 Paterna, Valencia, SpainFaculty of Sciences, University of Porto (FCUP), Rua do Campo Alegre, 4169-007 Porto, PortugalCONACYT-UAN, Secretary of Research and Postgraduate, Autonomous University of Nayarit, Tepic C.P., Nayarit 63155, MexicoBIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, 4485-661 Vairão, PortugalInstitute 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, PortugalFaculty of Sciences, University of Porto (FCUP), Rua do Campo Alegre, 4169-007 Porto, PortugalEarly 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 (10<sup>8</sup> CFU mL<sup>−1</sup>). 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’ 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–pathogen 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.https://www.mdpi.com/2073-4395/14/3/493plant diseasetomatokiwihyperspectral spectroscopygaussian process classificationwavelength selection |
| spellingShingle | Mafalda Reis Pereira Jochem Verrelst Renan Tosin Juan Pablo Rivera Caicedo Fernando Tavares Filipe Neves dos Santos Mário Cunha Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches Agronomy plant disease tomato kiwi hyperspectral spectroscopy gaussian process classification wavelength selection |
| title | Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches |
| title_full | Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches |
| title_fullStr | Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches |
| title_full_unstemmed | Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches |
| title_short | Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches |
| title_sort | plant disease diagnosis based on hyperspectral sensing comparative analysis of parametric spectral vegetation indices and nonparametric gaussian process classification approaches |
| topic | plant disease tomato kiwi hyperspectral spectroscopy gaussian process classification wavelength selection |
| url | https://www.mdpi.com/2073-4395/14/3/493 |
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