High-throughput phenotyping in maize and soybean genotypes using vegetation indices and computational intelligence

• Published in: Plant methods 2024-10, Vol.20 (1), p.164-13, Article 164
• Main Authors: Teodoro, Paulo E, Teodoro, Larissa P R, Baio, Fabio H R, Silva Junior, Carlos A, Santana, Dthenifer C, Bhering, Leonardo L
• Format: Article
• Language: English
• Subjects: Agricultural estimating and reporting; Agricultural research; Artificial intelligence; Artificial neural network; Corn; Crop yields; Genetic aspects; Genotype; Glycine max; Methods; Multispectral sensor; Phenotype; Plant breeding; Production processes; Soybean; Vegetation mapping; Zea mays
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-024-01294-0

Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping (HTP) is crucial to this goal. Our hypothesis is that vegetation indices (VIs) obtained from HTP can be used to indirectly measure agronomic variables in annual crops. The objectives were to study the association between agronomic variables in maize and soybean genotypes with VIs obtained from remote sensing and to identify computational intelligence for predicting GY of these crops from VIs as input in the models. Comparative trials were carried out with 30 maize genotypes in the 2020/2021, 2021/2022 and 2022/2023 crop seasons, and with 32 soybean genotypes in the 2021/2022 and 2022/2023 seasons. In all trials, an overflight was performed at R1 stage using the UAV Sensefly eBee equipped with a multispectral sensor for acquiring canopy reflectance in the green (550 nm), red (660 nm), near-infrared (735 nm) and infrared (790 nm) wavelengths, which were used to calculate the VIs assessed. Agronomic traits evaluated in maize crop were: leaf nitrogen content, plant height, first ear insertion height, and GY, while agronomic traits evaluated in soybean were: days to maturity, plant height, first pod insertion height, and GY. The association between the variables were expressed by a correlation network, and to identify which indices are best associated with each of the traits evaluated, a path analysis was performed. Lastly, VIs with a cause-and-effect association on each variable in maize and soybean trials were adopted as independent explanatory variables in multiple regression model (MLR) and artificial neural network (ANN), in which the 10 best topologies able to simultaneously predict all the agronomic variables evaluated in each crop were selected. Our findings reveal that VIs can be used to predict agronomic variables in maize and soybean. Soil-adjusted Vegetation Index (SAVI) and Green Normalized Dif-ference Vegetation Index (GNDVI) have a positive and high direct effect on all agronomic variables evaluated in maize, while Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) have a positive cause-and-effect association with all soybean variables. ANN outperformed MLR, p