The responsiveness and clinical utility of the Australian therapy outcome measure for indigenous clients.

Introduction: The Australian Therapy Outcome Measure for Indigenous Clients (ATOMIC) is a goal-setting tool designed to measure therapy outcomes with First Australians. It was originally developed and validated for use with First Australian children as a culturally responsive alternative to traditional western outcome measures. This research explored the applicability, responsiveness and clinical utility of the ATOMIC when used with First Australian adults attending an urban health service.

Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location.

Background: This study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols in the context of late-stage variety selection programs. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing programs.

Results: The research uncovered that integrating geometric and spectral traits led to an increase in prediction accuracy, whilst a recursive feature elimination (RFE) based variable selection workflowled to slight reductions in accuracy with the benefit of increased interpretability.

Mapping quantitative trait loci for seminal root angle in a selected durum wheat population.

Seminal root angle (SRA) is an important root architectural trait associated with drought adaptation in cereal crops. To date, all attempts to dissect the genetic architecture of SRA in durum wheat (Triticum durum Desf.) have used large association panels or structured mapping populations.

From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection.

Head (panicle) density is a major component in understanding crop yield, especially in crops that produce variable numbers of tillers such as sorghum and wheat. Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation, which is an inefficient and tedious process. Because of the easy availability of red-green-blue images, machine learning approaches have been applied to replacing manual counting.