Australian temperate pasture Genetic Resource Centres (GRCs) co-ordinated a major program to introduce and screen herbaceous forage species. This program aims to expand the environments where such species could reduce recharge and manage discharge for the control of dryland salinity in southern Australia. The sustainability of agriculture, in Australia especially, depends on continued access to new germplasm to enable plant breeders to continue crop and forage improvement. The GRCs supported the selection and identification of promising new legume, grass and herb species as part of a national pasture evaluation program. In total, 671 species and 21 non-species-specific genera were identified as having potential to increase water use profitability of recharge lands and to improve the productivity of saline lands across a diverse range of agricultural environments in southern Australia. Through a series of activities, 201 of these species, representing legumes, herbs and grasses were identified as promising. These were then disseminated for evaluation in a range of environments across southern Australia. The progress of selected species was monitored and germplasm of the most promising 11 species and three leguminous genera was targeted for intensive acquisition and characterisation as the basis for selection and breeding. In addition to the identification and dissemination of promising species of immediate potential, a comprehensive collection of 544 native and exotic, wild and cultivated pasture species was conserved and is now available to service future plant improvement programs.
Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew (Peronospora) on grapevine (Vitis vinifera) leaves at early stages of development using thermal imaging technology and to determine the best time during the day for image acquisition. In controlled experiments, 1587 thermal images of grapevines grown in a greenhouse were acquired around midday, before inoculation, 1, 2, 4, 5, 6, and 7 days after an inoculation. In addition, images of healthy and infected leaves were acquired at seven different times during the day between 7:00 a.m. and 4:30 p.m. Leaves were segmented using the active contour algorithm. Twelve features were derived from the leaf mask and from meteorological measurements. Stepwise logistic regression revealed five significant features used in five classification models. Performance was evaluated using K-folds cross-validation. The support vector machine model produced the best classification accuracy of 81.6%, F1 score of 77.5% and area under the curve (AUC) of 0.874. Acquiring images in the morning between 10:40 a.m. and 11:30 a.m. resulted in 80.7% accuracy, 80.5% F1 score, and 0.895 AUC.
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