A new multispectral index for canopy nitrogen concentration applicable across growth stages in ryegrass and barley - Manish Kumar Patel; Dongryeol Ryu; Andrew W. Western; Glenn J. Fitzgerald; Eileen M. Perry; Helen Suter; Iain M. Young (Precision Agriculture, 2024)
Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R 2 = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R 2 = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart.

Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs - Shirui Hao; Dongryeol Ryu; Andrew W Western; Eileen Perry; Heye Bogena; Harrie Jan Hendricks Franssen (Ecological Modelling, 2024)

Retrieving canopy nitrogen concentration and aboveground biomass with deep learning for ryegrass and barley: Comparing models and determining waveband contribution - Manish Kumar Patel; José Padarian; Andrew W. Western; Glenn J. Fitzgerald; Alex B. McBratney; Eileen M. Perry; Helen Suter; Dongryeol Ryu (Field Crops Research, 2023)

Proximal fluorescence sensing of potassium responsive crops to develop improved predictions of biomass, yield and grain quality of wheat and barley - Jonathan Eddison Holland; Davide Cammarano; Glenn J. Fitzgerald; Eileen M. Perry; Graeme Poile; Mark Kenneth Conyers (Precision Agriculture, 2019)

Mother–infant interactions and regional brain volumes in infancy: an MRI study - Vaheshta Sethna; Inês Pote; Siying Wang; Maria Gudbrandsen; Anna Blasi; Caroline McCusker; Eileen Daly; Emily Perry; Kerrie P. H. Adams; Maria Kuklisova-Murgasova; Paula Busuulwa; Sarah Lloyd-Fox; et al. (Brain Structure and Function, 2017)

FUS Immunogold Labeling TEM Analysis of the Neuronal Cytoplasmic Inclusions of Neuronal Intermediate Filament Inclusion Disease: A Frontotemporal Lobar Degeneration with FUS Proteinopathy - Tristan Page; Michael A. Gitcho; Sabrina Mosaheb; Deborah Carter; Sumi Chakraverty; Robert H. Perry; Eileen H. Bigio; Marla Gearing; Isidre Ferrer; Alison M. Goate; Nigel J. Cairns; Julian R. Thorpe (Journal of Molecular Neuroscience, 2011)

Spatial variation in tree characteristics and yield in a pear orchard - Eileen M. Perry; Raymond J. Dezzani; Clark F. Seavert; Francis J. Pierce (Precision Agriculture, 2010)