Go to:
Logótipo
Comuta visibilidade da coluna esquerda
Você está em: Start > Publications > View > Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches
Publication

Publications

Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches

Title
Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches
Type
Article in International Scientific Journal
Year
2024
Authors
Pereira, MR
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Verrelst, J
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Caicedo, JPR
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Fernando Tavares
(Author)
FCUP
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page View ORCID page
Mario Cunha
(Author)
FCUP
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page Without ORCID
Journal
The Journal is awaiting validation by the Administrative Services.
Title: AGRONOMY-BASELImported from Authenticus Search for Journal Publications
Vol. 14
Final page: 493
ISSN: 2073-4395
Indexing
Publicação em ISI Web of Knowledge ISI Web of Knowledge - 0 Citations
Publicação em Scopus Scopus - 0 Citations
Other information
Authenticus ID: P-00Z-ZND
Abstract (EN): 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-1). 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.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 25
Documents
We could not find any documents associated to the publication.
Related Publications

Of the same journal

Smartphone Applications Targeting Precision Agriculture Practices-A Systematic Review (2020)
Another Publication in an International Scientific Journal
Mendes, J; Pinho, TM; Filipe Neves Santos; Sousa, JJ; Peres, E; Boaventura Cunha, J; Mario Cunha; Morais, R
Legume Biofortification and the Role of Plant Growth-Promoting Bacteria in a Sustainable Agricultural Era (2020)
Another Publication in an International Scientific Journal
Roriz, M; Carvalho, SMP; Castro, PML; Vasconcelos, MW
Temperature-Based Grapevine Ripeness Modeling for cv. Touriga Nacional and Encruzado in the Dao Wine Region, Portugal (2021)
Article in International Scientific Journal
Rodrigues, P; Pedroso, V; Goncalves, F; Reis, S; Santos, JA
QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps (2021)
Article in International Scientific Journal
Lia Duarte; Ana Teodoro; Sousa, JJ; Padua, L
Potential Non-Invasive Technique for Accessing Plant Water Contents Using a Radar System (2021)
Article in International Scientific Journal
Luís Carlos Santos; Filipe Neves dos Santos; Raul Morais; Cândido Duarte

See all (14)

Recommend this page Top
Copyright 1996-2025 © Faculdade de Direito da Universidade do Porto  I Terms and Conditions  I Acessibility  I Index A-Z
Page created on: 2025-07-24 at 23:06:48 | Privacy Policy | Personal Data Protection Policy | Whistleblowing