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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
16/06/2025 |
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Data da última atualização: |
16/06/2025 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA, D. C.; MADARI, B. E.; CARVALHO, M. da C. S.; FERREIRA, M. E. |
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Afiliação: |
DIOGO CASTILHO SILVA, UNIVERSIDADE FEDERAL DE GOIÁS; BEATA EMOKE MADARI, CNPAF; MARIA DA CONCEICAO SANTANA CARVALHO, CNPAF; MANUEL EDUARDO FERREIRA, UNIVERSIDADE FEDERAL DE GOIÁS. |
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Título: |
Optimizing nitrogen estimates in common bean canopies throughout key growth stages via spectral and textural data from unmanned aerial vehicle multispectral imagery. |
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Ano de publicação: |
2025 |
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Fonte/Imprenta: |
European Journal of Agronomy, v. 169, 127697, 2025. |
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ISSN: |
1161-0301 |
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DOI: |
https://doi.org/10.1016/j.eja.2025.127697 |
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Idioma: |
Inglês |
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Conteúdo: |
Leaf nitrogen assessment is crucial for optimizing crop management, driving remote sensing use. This study demonstrates the effectiveness of unmanned aerial vehicles (UAVs) multispectral imagery for enhancing leaf nitrogen content estimation in common bean (Phaseolus vulgaris L.) through the integration of vegetation indices (VIs) and texture features. Research conducted over two years (2021–2022) evaluated various nitrogen rates across critical growth stages (V4, R5, and R7). Machine learning models combining spectral and textural information significantly outperformed single-index approaches, achieving root mean square error (RMSE) values of 1.80 g kg−1 (relative root mean square error – RRMSE = 2.93 %) at V4 stage using support vector machine with VIs, and 2.79 g kg−1 (RRMSE = 5.20 %) at R5 stage using random forest with VIs. For later growth stages (R7) and across the entire season (all growth stages), the combination of VIs and texture metrics proved most effective, with random forest achieving RMSE values of 3.42 and 3.96 g kg−1 (RRMSE = 7.40 and 7.32 %), respectively. Texture analysis in across-row directions (90° and 135°) provided superior performance compared to traditional diagonal approaches for row-planted crops. Linear regression analysis showed that normalized difference texture indices incorporating correlation and homogeneity explained up to 71 % of leaf nitrogen content variability at R7 stage. The optimal nitrogen rate of 91 kg ha−1, validated through both yield response and leaf nitrogen measurements, provides a robust benchmark for nitrogen management in common bean production. This methodology offers a practical framework for real-time, site-specific nitrogen management that improves upon current recommendation systems. MenosLeaf nitrogen assessment is crucial for optimizing crop management, driving remote sensing use. This study demonstrates the effectiveness of unmanned aerial vehicles (UAVs) multispectral imagery for enhancing leaf nitrogen content estimation in common bean (Phaseolus vulgaris L.) through the integration of vegetation indices (VIs) and texture features. Research conducted over two years (2021–2022) evaluated various nitrogen rates across critical growth stages (V4, R5, and R7). Machine learning models combining spectral and textural information significantly outperformed single-index approaches, achieving root mean square error (RMSE) values of 1.80 g kg−1 (relative root mean square error – RRMSE = 2.93 %) at V4 stage using support vector machine with VIs, and 2.79 g kg−1 (RRMSE = 5.20 %) at R5 stage using random forest with VIs. For later growth stages (R7) and across the entire season (all growth stages), the combination of VIs and texture metrics proved most effective, with random forest achieving RMSE values of 3.42 and 3.96 g kg−1 (RRMSE = 7.40 and 7.32 %), respectively. Texture analysis in across-row directions (90° and 135°) provided superior performance compared to traditional diagonal approaches for row-planted crops. Linear regression analysis showed that normalized difference texture indices incorporating correlation and homogeneity explained up to 71 % of leaf nitrogen content variability at R7 stage. The optimal nitrogen rate of 91 kg ha−1, validated through both... Mostrar Tudo |
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Palavras-Chave: |
Machine learning. |
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Thesagro: |
Agricultura de Precisão; Feijão; Phaseolus Vulgaris. |
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Thesaurus Nal: |
Beans; Multispectral imagery; Precision agriculture; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02724naa a2200277 a 4500
001 2176626
005 2025-06-16
008 2025 bl uuuu u00u1 u #d
022 $a1161-0301
024 7 $ahttps://doi.org/10.1016/j.eja.2025.127697$2DOI
100 1 $aSILVA, D. C.
245 $aOptimizing nitrogen estimates in common bean canopies throughout key growth stages via spectral and textural data from unmanned aerial vehicle multispectral imagery.$h[electronic resource]
260 $c2025
520 $aLeaf nitrogen assessment is crucial for optimizing crop management, driving remote sensing use. This study demonstrates the effectiveness of unmanned aerial vehicles (UAVs) multispectral imagery for enhancing leaf nitrogen content estimation in common bean (Phaseolus vulgaris L.) through the integration of vegetation indices (VIs) and texture features. Research conducted over two years (2021–2022) evaluated various nitrogen rates across critical growth stages (V4, R5, and R7). Machine learning models combining spectral and textural information significantly outperformed single-index approaches, achieving root mean square error (RMSE) values of 1.80 g kg−1 (relative root mean square error – RRMSE = 2.93 %) at V4 stage using support vector machine with VIs, and 2.79 g kg−1 (RRMSE = 5.20 %) at R5 stage using random forest with VIs. For later growth stages (R7) and across the entire season (all growth stages), the combination of VIs and texture metrics proved most effective, with random forest achieving RMSE values of 3.42 and 3.96 g kg−1 (RRMSE = 7.40 and 7.32 %), respectively. Texture analysis in across-row directions (90° and 135°) provided superior performance compared to traditional diagonal approaches for row-planted crops. Linear regression analysis showed that normalized difference texture indices incorporating correlation and homogeneity explained up to 71 % of leaf nitrogen content variability at R7 stage. The optimal nitrogen rate of 91 kg ha−1, validated through both yield response and leaf nitrogen measurements, provides a robust benchmark for nitrogen management in common bean production. This methodology offers a practical framework for real-time, site-specific nitrogen management that improves upon current recommendation systems.
650 $aBeans
650 $aMultispectral imagery
650 $aPrecision agriculture
650 $aVegetation index
650 $aAgricultura de Precisão
650 $aFeijão
650 $aPhaseolus Vulgaris
653 $aMachine learning
700 1 $aMADARI, B. E.
700 1 $aCARVALHO, M. da C. S.
700 1 $aFERREIRA, M. E.
773 $tEuropean Journal of Agronomy$gv. 169, 127697, 2025.
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