Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation.

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

Barley grass (Hordeum murinum subsp. glaucum.) is an annual weed associated with grain revenue loss and sheep carcass damage in southern Australia. Increasing herbicide resistance led to a recent investigation into effective integrated weed management strategies for barley grass in southern Australia. Field studies in Wagga Wagga, New South Wales (NSW) during 2016 and 2017 examined the effect of post-emergent herbicide applications and strategic defoliation by mowing on barley grass survival and seed production in a mixed legume pasture. Statistically significant differences between herbicide-only treatments in both years showed propaquizafop to be more than 98% effective in reducing barley grass survival and seed production. Paraquat was not effective in controlling barley grass (58% efficacy), but led to a 36% and 63.5% decrease in clover and other weed biomass, respectively, after 12 months and increased lucerne biomass by over three-fold after 24 months. A single repeated mowing treatment resulted in a 46% decline in barley grass seedling emergence after 12 months and, when integrated with herbicide applications, reduced other weed biomass after 24 months by 95%. Resistance to acetyl-CoA carboxylase (ACCase)-inhibiting herbicides observed in local barley grass populations led to additional and more focused investigation comparing the efficacy of other pre- and post-emergent herbicides for barley grass management in legume pastures. Haloxyfop-R + simazine or paraquat, applied at early tillering stage, were most efficacious in reducing barley grass survival and fecundity. Impact of defoliation timing and frequency on barley grass seedlings was also evaluated at various population densities, highlighting the efficacy of repeated post-inflorescence defoliations in reducing plant survival and seed production. Results highlight the importance of optimal environmental conditions and application timing in achieving efficacious control of barley grass and improving pasture growth and biomass accumulation.

Seed contamination significantly affects production capacity and animal welfare in Australian sheep flocks and causes considerable financial loss to producers and processors across sheepmeat value chains. Seven grass-weed species contribute to seed contamination in Australia, with barley grass (Hordeum spp.) identified as a key perpetrator. Herbicide resistance and variable dormancy emerging in southern Australian barley grass populations are thought to enhance its capacity for successful pasture invasion, further exacerbating the potential for seed contamination in sheep. The present article reviews the current literature regarding the impact and incidence of seed contamination on sheepmeat production, with particular reference to key grass-weed species prevalence across Australia. Data are presented on a recent incidence of carcass contamination across years, where incidence varied between 11% and 80% from 2009 to 2013, contracting to between 2% and 60% during 2014 and 2015. Key areas requiring future research are defined. Understanding the biology of key grass weeds, historical influences and economic consequences associated with seed contamination in sheep may assist in defining future risks to sheep production and improve weed management. Furthermore, examining more recent data describing the current status of seed contamination across Australia and the associations with causal weed species may aid the development of critical weed-management strategies in highly infested regions, subsequently limiting the extent of future seed contamination.