Enhancing Kiwi Bacterial Canker Leaf Assessment: Integrating Hyperspectral-Based Vegetation Indexes in Predictive Modeling ¿

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Reis Pereira, M (Autor) Outra; Tosin, R (Autor) FCUP; Martins, C (Autor) Outra; Dos Santos, FN (Autor) Outra; Fernando Tavares (Autor) FCUP; Mario Cunha (Autor) FCUP

Revista

Título: Engineering proceedings

Vol. 48

ISSN: 2673-4591

Indexação

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00Z-6XH

DOI: 10.3390/csac2023-14920

Abstract (EN): The potential of hyperspectral UV¿VIS¿NIR reflectance for the in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325¿1075 nm) of twenty kiwi plants were obtained in vivo and in situ with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal over 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV¿VIS¿NIR reflectance for the non-destructive discrimination of bacterial canker on kiwi leaves. © 2023 by the authors.

Idioma: Inglês

Tipo (Avaliação Docente): Científica