Nascimento José Henrique Bernardino, Cortes Diego Fernando Marmolejo, Andrade Luciano Rogerio Braatz de, Gallis Rodrigo Bezerra de Araújo, Barbosa Ricardo Luis, Oliveira Eder Jorge de
Centro de Ciências Agrárias, Ambientais e Biológicas, Universidade Federal do Recôncavo da Bahia, Cruz das Almas 44380-000, Bahia, Brazil.
Embrapa Mandioca e Fruticultura, Nugene, Cruz das Almas 44380-000, Bahia, Brazil.
Plants (Basel). 2024 Dec 25;14(1):32. doi: 10.3390/plants14010032.
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 ( = 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 ≤ ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ HCullis2 ≤ 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 (R2 = 0.26 at 20 m and R2 = 0.44 at 60 m), while plant vigor ranged from R2 = 0.50 at 20 m to R2 = 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.
使用无人机（UAV）进行大规模表型分析被认为是植物选择的重要工具。本研究旨在估计不同飞行高度下获得的农艺数据与植被指数（VI）之间的相关性，并选择预测模型以评估航空成像在木薯育种计划中的潜在用途。使用混合模型获得并分析了各种VI，以得出最佳线性无偏预测值、遗传力参数以及与各种农艺性状的相关性。这些VI还用于构建农艺性状的预测模型。航空成像显示，无论飞行高度如何，估计株高的潜力都很高（ = 0.99），尽管低空飞行（20米）对该性状的估计偏差较小。与RGB相比，多光谱传感器显示出更高的相关性，尤其是对于活力、地上部产量和鲜根产量（-0.40≤ ≤0.50）。无论使用何种传感器，不同飞行高度下VI的遗传力范围从中等至高（0.51≤HCullis2≤0.94）。使用具有逐步特征选择的广义线性模型（GLMSS）和K近邻（KNN）模型，观察到对植物活力和干物质含量性状的最佳预测模型。对于GLMSS模型，干物质含量的预测能力随飞行高度增加（20米时R2 = 0.26，60米时R2 = 0.44），而在KNN模型中，植物活力的R2范围从20米时的0.50到40米时的0.47。我们的结果表明，通过航空成像实施高通量表型分析在育种计划中进行快速高效选择具有实际潜力。
