Rattling modes and the intrinsic vibrational spectrum of beetle-type scanning tunneling microscopes

It is known that the vibrational spectra of beetle-type scanning tunneling microscopes with a total mass of ≈3–4 g contain extrinsic ‘rattling’ modes in the frequency range extending from 500 to 1700 Hz that interfere with image acquisition. These modes lie below the lowest calculated eigenfrequency of the beetle and it has been suggested that they arise from the inertial sliding of the beetle between surface asperities on the raceway. In this paper we describe some cross-coupling measurements that were performed on three home-built beetle-type STMs of two different designs. We provide evidence that suggests that for beetles with total masses of 12–15 g all the modes in the rattling range are intrinsic. This provides additional support for the notion that the vibrational properties of beetle-type scanning tunneling microscopes can be improved by increasing the contact pressure between the feet of the beetle and the raceway.

Prey of the silver-headed antechinus (Antechinus argentus), a new species of Australian dasyurid marsupial

The silver-headed antechinus (Antechinus argentus) is one of Australia’s most recently described mammals, and the single known population at Kroombit Tops in south-east Queensland is threatened. Nothing is known of the species’ ecology, so during 2014 we collected faecal pellets each month (March–September) from a population at the type locality to gather baseline data on diet composition. A total of 38 faecal pellets were collected from 12 individuals (eight females, four males) and microscopic analysis of pellets identified seven invertebrate orders, with 70% combined mean composition of beetles (Coleoptera: 38%) and cockroaches (Blattodea: 32%). Other orders that featured as prey were ants, crickets/grasshoppers, butterflies/moths, spiders, and true bugs. Given that faecal pellets could only be collected from a single habitat type (Eucalyptus montivaga high-altitude open forest) and location, this is best described as a generalist insectivorous diet that is characteristic of other previously studied congeners.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

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 technology 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 the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling 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. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.