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.

Epigenetics Underpinning DNA Damage Repair

Epigenetics is the study of heritable changes in gene expression that are not the result of genetic alterations. These changes include DNA methylation, histone modifications, or indeed microRNA expression. Chromatin is a tightly compacted DNA–protein complex that allows approximately two meters of DNA to be packaged inside a cell, only a few micrometers across. Although the resulting DNA structure is very stable, it is not very amiable to DNA-dependent processes, so mechanisms have to exist to allow processes such as transcription, replication, and DNA repair to occur. This chapter will look at how a cell responds to and deals with genomic instability at the epigenetic level and highlight how critical chromatin remodeling is for correct DNA repair and cell survival following DNA damage. This chapter will initially look at the DNA repair pathways that function in human cells and then at how the repair of DNA damage is controlled by epigenetics.

Vibration characteristics and damage detection in a suspension bridge

Suspension bridges are flexible and vibration sensitive structures that exhibit complex and multi-modal vibration. Due to this, the usual vibration based methods could face a challenge when used for damage detection in these structures. This paper develops and applies a mode shape component specific damage index (DI) to detect and locate damage in a suspension bridge with pre-tensioned cables. This is important as suspension bridges are large structures and damage in them during their long service lives could easily go un-noticed. The capability of the proposed vibration based DI is demonstrated through its application to detect and locate single and multiple damages with varied locations and severity in the cables of the suspension bridge. The outcome of this research will enhance the safety and performance of these bridges which play an important role in the transport network.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

Case management in action: An examination of two cases in the area of alcohol related brain damage

‘Practice Forum’ provides a forum for social work practitioners to share their practice with others; to describe what they are doing and assess its effectiveness. The practice of case management is applied in a wide range of service delivery models to meet complex client needs. Unfortunately, cost containment and lack of clarity of the role of the case manager has blurred the definition and practice of case management for both the consumer and professional providers. This article examines two cases of a small non-government agency in Melbourne called Alcohol Related Brain Injury Assessment, Accommodation & Support Inc. (ARBIAS) where case management services are delivered to people with alcohol acquired brain damage. The analysis presented here supports the view that continuity of care and intensive relationship building with clients is vital for successful client outcomes and has application to a variety of programs which service chronically disabled clients.