CRC30032 Enhancing surveillance with remotely controlled aircraft

Detecting spores with UAV.

Reproductive interference between honey bee species in artificial sympatry

Reproductive isolation between closely related species is often incomplete. The Western honey bee, Apis mellifera, and the Eastern hive bee, A. cerana have been allopatric for millions of years, but are nonetheless similar in morphology and behaviour. During the last century the two species were brought into contact anthropogenically, providing potential opportunities for interspecific matings. Hybrids between A. mellifera and A. cerana are inviable, so natural interspecific matings are of concern because they may reduce the viability of A. cerana and A. mellifera populations – two of the world's most important pollinators. We examined the mating behaviour of A. mellifera and A. cerana queens and drones from Caoba Basin, China and Cairns, Australia. Drone mating flight times overlap in both areas. Analysis of the spermathecal contents of queens with species-specific genetic markers indicated that in Caoba Basin, 14% of A. mellifera queens mated with at least one A. cerana male, but we detected no A. cerana queens that had mated with A. mellifera males. Similarly, in Cairns, no A. cerana queens carried A. mellifera sperm, but one third of A. mellifera queens had mated with at least one A. cerana male. No hybrid embryos were detected in eggs laid by interspecifically-mated A. mellifera queens in either location. However A. mellifera queens artificially inseminated with A. cerana sperm produced inviable hybrid eggs, or unfertilised drones. This suggests that reproductive interference will impact the viability of honey bee populations wherever A. cerana and A. mellifera are in contact. This article is protected by copyright. All rights reserved.

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.