Machine learning for unmanned aerial vehicles-based soybean phenotyping: limits of cross-environment transfer and opportunities to reduce field measurements

High-throughput phenotyping using unmanned aerial vehicles (UAVs) and spectral vegetation indices has been proposed to overcome the cost and logistical constraints of manual measurements in multi-environment breeding trials. However, the reliability of models trained on spectr...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	João Amaro Ferreira Vieira Netto, Hernandes Peres Panichi, Paulo Eduardo Teodoro, Leonardo Lopes Bhering
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Universidade Federal de Goiás 2026-04-01
Sarja:	Pesquisa Agropecuária Tropical
Aiheet:	glycine max multispectral remote sensing canopy architecture
Linkit:	https://revistas.ufg.br/pat/article/view/84508/45062
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