Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery. (26th July 2019)

• Record Type: Journal Article
• Authors: Borra‐Serrano, Irene; De Swaef, Tom; Muylle, Hilde; Nuyttens, David; Vangeyte, Jürgen; Mertens, Koen; Saeys, Wouter; Somers, Ben; Roldán‐Ruiz, Isabel; Lootens, Peter
• Is Part Of: Grass and forage science. Volume 74:Number 3(2019)
• Page Start: 356
• Page End: 369
• Publication Date: 2019-07-26
• Subjects: biomass -- drone -- dry‐matter yield -- forage -- RGB sensor; Grasses -- Periodicals; Forage plants -- Periodicals; Grasslands -- Periodicals; 633.2
• DOI: 10.1111/gfs.12439
• Languages: English
• ISSNs: 0142-5242

Abstract

Abstract: In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression ( R 2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process.