Resumen

PEREZ, O. M.. High-throughput phenotyping using aerial images for predicting agronomic traits in soybean breeding programs. Agrocienc. Urug. [online]. 2025, vol.29, e1530.  Epub 01-Dic-2025. ISSN 2730-5066.  https://doi.org/10.31285/agro.29.1530.

Plant breeding programs know the advantages of high-throughput phenotyping (HTP) in increasing efficiency over classical phenotyping and screening methods, which is achieved by saving time and improving selection accuracy. Even so, most programs have not yet systematically implemented this technology into their breeding pipelines. This review aims to indicate the restrictions of implementing HTP at a large scale and to summarize studies according to the used devices, data classes collected, and artificial intelligence (AI) methods applied to predict and classify agronomic traits in plant breeding programs with a focus on soybean (Glycine max (L.) Merr.). Excluding HTP platforms in laboratories and greenhouses, satellite remote sensing, and autonomous mobile robots, this review focuses on field-based HTP platforms that take aerial images from drones and apply AI methods to associate those images with the traits of interest. Field-based HTP research is also conducted using hand-held devices that record individual vegetation indices (e.g., NDVI), a few spectral bands (multispectral radiometers), or the continuous range of the electromagnetic light spectrum (spectroradiometers). However, plant breeders must evaluate thousands of experimental lines each year, so using these devices instead of drones implies a trade-off between acquisition accuracy and the time it takes to collect the data. A challenge in the coming years is fine-tuning scalable, reliable models and optimizing data input, processing, and output pipelines to provide breeders with helpful information before they make selections.

Palabras clave : digital agriculture; machine learning; phenomics; remote sensing; soybean.

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