Proximal sensing of Urochloa grasses increases selection accuracy

Juan De La Cruz Jiménez, Luisa Leiva, Juan A. Cardoso, Andrew N. French, Kelly R. Thorp

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughput phenotyping offers a rapid and economical means of evaluating large numbers of genotypes. In this study, visual assessments, digital colour images, and spectral reflectance data were collected from 200 Urochloa hybrids in a field setting. Partial least-squares regression (PLSR) was applied to relate visual assessments, digital image analysis and spectral data to shoot dry weight, crude protein and chlorophyll concentrations. Visual evaluations of biomass and greenness were collected in 68 min, digital colour imaging data in 40 min, and hyperspectral canopy data in 80 min. Root-mean-squared errors of prediction for PLSR estimations of shoot dry weight, crude protein and chlorophyll were lowest for digital image analysis followed by hyperspectral analysis and visual assessments. This study showed that digital colour image and spectral analysis techniques have the potential to improve precision and reduce time for tropical forage grass phenotyping.

Original languageEnglish
Pages (from-to)401-409
Number of pages9
JournalCrop and Pasture Science
Issue number4
Publication statusPublished - Apr 2020


Dive into the research topics of 'Proximal sensing of Urochloa grasses increases selection accuracy'. Together they form a unique fingerprint.

Cite this