Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing
Blueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to chara...
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MDPI AG
2024-09-01
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| author | Sérgio H. G. Silva Marcelo C. Berardo Lucas R. Rosado Renata Andrade Anita F. S. Teixeira Mariene H. Duarte Fernanda A. Bócoli Marco A. C. Carneiro Nilton Curi |
| author_facet | Sérgio H. G. Silva Marcelo C. Berardo Lucas R. Rosado Renata Andrade Anita F. S. Teixeira Mariene H. Duarte Fernanda A. Bócoli Marco A. C. Carneiro Nilton Curi |
| author_sort | Sérgio H. G. Silva |
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| description | Blueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to characterize the nutrient contents of healthy plants of three blueberry varieties adapted to warm climates (Emerald, Jewel, and Biloxi) using a reference method for foliar analysis (inductively coupled plasma (ICP)) and a portable X-ray fluorescence (pXRF) spectrometer on fresh and dry leaves and (ii) to differentiate blueberry varieties based on their nutrient composition. Nutrient content was statistically compared per leaf moisture condition (fresh or dry) with ICP results and used to differentiate the varieties via the random forest algorithm. P and Zn contents (ICP) in leaves were different among varieties. Dry leaf results (pXRF) were strongly correlated with ICP results. Most nutrients determined using ICP presented good correlation with pXRF data (R<sup>2</sup> from 0.66 to 0.93). The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. This approach can also be applied to other crops to facilitate nutrient assessment in leaves. |
| format | Article |
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| language | English |
| publishDate | 2024-09-01 |
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| spelling | doaj-art-622cd0768acb40549400e72d01c4e43e2025-08-20T01:56:09ZengMDPI AGAgriEngineering2624-74022024-09-01633187320210.3390/agriengineering6030182Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal SensingSérgio H. G. Silva0Marcelo C. Berardo1Lucas R. Rosado2Renata Andrade3Anita F. S. Teixeira4Mariene H. Duarte5Fernanda A. Bócoli6Marco A. C. Carneiro7Nilton Curi8Department of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilFaculdades Londrina, Av. Duque de Caxias, 450, Centro Cívico, Londrina 86015-000, PR, BrazilDepartment of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilDepartment of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilDepartment of Agriculture, Federal University of Lavras, Lavras 37200-900, MG, BrazilDepartment of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilDepartment of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilDepartment of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilDepartment of Soil Science, Federal University of Lavras, Lavras 37200-900, MG, BrazilBlueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to characterize the nutrient contents of healthy plants of three blueberry varieties adapted to warm climates (Emerald, Jewel, and Biloxi) using a reference method for foliar analysis (inductively coupled plasma (ICP)) and a portable X-ray fluorescence (pXRF) spectrometer on fresh and dry leaves and (ii) to differentiate blueberry varieties based on their nutrient composition. Nutrient content was statistically compared per leaf moisture condition (fresh or dry) with ICP results and used to differentiate the varieties via the random forest algorithm. P and Zn contents (ICP) in leaves were different among varieties. Dry leaf results (pXRF) were strongly correlated with ICP results. Most nutrients determined using ICP presented good correlation with pXRF data (R<sup>2</sup> from 0.66 to 0.93). The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. This approach can also be applied to other crops to facilitate nutrient assessment in leaves.https://www.mdpi.com/2624-7402/6/3/182berriespXRFfoliar analysiselemental composition |
| spellingShingle | Sérgio H. G. Silva Marcelo C. Berardo Lucas R. Rosado Renata Andrade Anita F. S. Teixeira Mariene H. Duarte Fernanda A. Bócoli Marco A. C. Carneiro Nilton Curi Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing AgriEngineering berries pXRF foliar analysis elemental composition |
| title | Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing |
| title_full | Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing |
| title_fullStr | Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing |
| title_full_unstemmed | Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing |
| title_short | Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing |
| title_sort | advancing leaf nutritional characterization of blueberry varieties adapted to warm climates enhanced by proximal sensing |
| topic | berries pXRF foliar analysis elemental composition |
| url | https://www.mdpi.com/2624-7402/6/3/182 |
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