High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging
Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate...
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2024-12-01
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| author | José Henrique Bernardino Nascimento Diego Fernando Marmolejo Cortes Luciano Rogerio Braatz de Andrade Rodrigo Bezerra de Araújo Gallis Ricardo Luis Barbosa Eder Jorge de Oliveira |
| author_facet | José Henrique Bernardino Nascimento Diego Fernando Marmolejo Cortes Luciano Rogerio Braatz de Andrade Rodrigo Bezerra de Araújo Gallis Ricardo Luis Barbosa Eder Jorge de Oliveira |
| author_sort | José Henrique Bernardino Nascimento |
| collection | DOAJ |
| description | Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (<i>r</i> = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ <i>r</i> ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>H</mi></mrow><mrow><mi>C</mi><mi>u</mi><mi>l</mi><mi>l</mi><mi>i</mi><mi>s</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.26 at 20 m and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.44 at 60 m), while plant vigor ranged from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.50 at 20 m to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs. |
| format | Article |
| id | doaj-art-a081b83bbb7f403b8b19cff4c7886703 |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-a081b83bbb7f403b8b19cff4c78867032025-01-10T13:19:32ZengMDPI AGPlants2223-77472024-12-011413210.3390/plants14010032High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial ImagingJosé Henrique Bernardino Nascimento0Diego Fernando Marmolejo Cortes1Luciano Rogerio Braatz de Andrade2Rodrigo Bezerra de Araújo Gallis3Ricardo Luis Barbosa4Eder Jorge de Oliveira5Centro de Ciências Agrárias, Ambientais e Biológicas, Universidade Federal do Recôncavo da Bahia, Cruz das Almas 44380-000, Bahia, BrazilCentro de Ciências Agrárias, Ambientais e Biológicas, Universidade Federal do Recôncavo da Bahia, Cruz das Almas 44380-000, Bahia, BrazilEmbrapa Mandioca e Fruticultura, Nugene, Cruz das Almas 44380-000, Bahia, BrazilInstituto de Geografia, Universidade Federal de Uberlândia, Av. João Naves de Ávila, 2121—Bairro Santa Mônica, Uberlândia 38408-902, Minas Gerais, BrazilInstituto de Geografia, Universidade Federal de Uberlândia, Av. João Naves de Ávila, 2121—Bairro Santa Mônica, Uberlândia 38408-902, Minas Gerais, BrazilEmbrapa Mandioca e Fruticultura, Nugene, Cruz das Almas 44380-000, Bahia, BrazilLarge-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (<i>r</i> = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ <i>r</i> ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>H</mi></mrow><mrow><mi>C</mi><mi>u</mi><mi>l</mi><mi>l</mi><mi>i</mi><mi>s</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.26 at 20 m and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.44 at 60 m), while plant vigor ranged from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.50 at 20 m to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs.https://www.mdpi.com/2223-7747/14/1/32<i>Manihot esculenta</i> CrantzUAVspredictionvegetation indices |
| spellingShingle | José Henrique Bernardino Nascimento Diego Fernando Marmolejo Cortes Luciano Rogerio Braatz de Andrade Rodrigo Bezerra de Araújo Gallis Ricardo Luis Barbosa Eder Jorge de Oliveira High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging Plants <i>Manihot esculenta</i> Crantz UAVs prediction vegetation indices |
| title | High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging |
| title_full | High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging |
| title_fullStr | High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging |
| title_full_unstemmed | High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging |
| title_short | High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging |
| title_sort | high throughput phenotyping for agronomic traits in cassava using aerial imaging |
| topic | <i>Manihot esculenta</i> Crantz UAVs prediction vegetation indices |
| url | https://www.mdpi.com/2223-7747/14/1/32 |
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