| Texto completo | |
| Autor(es): Mostrar menos - |
Osco, Lucas Prado
;
Moriya, Erika Akemi Saito
;
de Lima, Bruna Coelho
;
Ramos, Ana Paula Marques
;
Marcato Junior, Jose
;
Goncalves, Wesley Nunes
;
Jorge, Lucio Andre de Castro
;
Liesenberg, Veraldo
;
Li, Jonathan
;
de Araujo, Ademir Sergio Ferreira
;
Imai, Nilton Nobuhiro
;
de Araujo, Fabio Fernando
Número total de Autores: 12
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| Tipo de documento: | Artigo Científico |
| Fonte: | AGRIENGINEERING; v. 7, n. 11, p. 18-pg., 2025-11-07. |
| Resumo | |
Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning models for detecting thermal stress remain an open research question. This study presents a robust framework that utilizes eight state-of-the-art machine learning algorithms to classify the reflectance response of thermal-induced stress in two cultivars of bean plants. Our controlled experiment measured hyperspectral data across two growth stages and three stress conditions (pre-stress, during stress, and post-stress) using a spectroradiometer. The results demonstrate the high performance of several algorithms, with the Artificial Neural Network (ANN) achieving an impressive 99.4% overall accuracy. A key contribution of this work is the identification of the most contributory spectral ranges for thermal stress discrimination: the green region (530-570 nm) and the red-edge region (700-710 nm). This framework is a feasible and effective tool for modelling the hyperspectral response of thermal-stressed bean plants and provides critical guidance for future research on stress-specific spectral indices. (AU) | |
| Processo FAPESP: | 17/16202-2 - Bacillus subtilis na atenuação do estresse térmico e modulação do etileno em feijoeiro |
| Beneficiário: | Fabio Fernando de Araujo |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |