"Description of blue tongue and other arboviruses in Northern Australia"

A serological survey of cattle from throughout Queensland and sheep from cattle/sheep interface areas was conducted to determine the distribution and prevalence of antibodies to Bluetongue virus serotypes. This information allowed preliminary designation of arbovirusfree zones and identification of livestock populations at greatest risk to introduction of exotic Bluetongue viruses. Throughout the state antibodies were detected to only serotypes I and 21. In cattle prevalence decreased with increasing distance from the coast ringing from 73% in the far north to less than I% in the southwest. In sheep, prevalence of bluetongue antibodies in the major cattle/sheep interface areas in the north-west and central Queensland ranged from O% to 5%. A system of strategically placed sentinel herds of 10 young serologically negative cattle was established across northern Australia to monitor the distribution and seasonality of bluetongue viruses. Initially 23 herds were located in Queensland, 4 in Northern Territory and 2 in Western Australia but by the completion of the project the number of herds in Queensland had been reduced to 12. No bluetongue virus activity was detected in Western Australia or Northern Territory herds throughout the project although testing of one herd in Northern Territory with a history of bluetongue activity was not done after June 1991. In Queensland, activity to bluetongue serotypes I and 21 was detected in all years of the project. Transmissions occurred predominantly in the period April to September and were more widespread in wetter years' The pathogenic bluetongue setotypes previously isolated from the Northern Territory have not spread to adjoining States.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.