Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/57077
Título: A proximal sensor-based approach for clean, fast, and accurate assessment of the Eucalyptus spp. nutritional status and differentiation of clones
Palavras-chave: Portable X-ray fluorescence (pXRF) spectrometry
Proximal sensing
Machine learning
Leaf nutrient analysis
Greentech analysis
Eucalyptus cultivation
Plant mineral nutrition
Data do documento: 2023
Editor: Multidisciplinary Digital Publishing Institute
Citação: ANDRADE, R. et al. A proximal sensor-based approach for clean, fast, and accurate assessment of the Eucalyptus spp. nutritional status and differentiation of clones. Plants, [S.l.], v. 12, n. 3, 2023.
Resumo: Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.
URI: https://www.mdpi.com/2223-7747/12/3/561
http://repositorio.ufla.br/jspui/handle/1/57077
Aparece nas coleções:DCS - Artigos publicados em periódicos

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