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.

Traversing the space between threats and violence: A review of threat assessment guidelines

While the majority of violent threats – defined as an expression of intent to do harm or act out violently against someone or something – do not progress to actual violence, a small proportion of threateners do go on to enact violence. Most researchers argue that violence risk assessments are inadequate for assessing threats of violence, which raises the question: how should a threat assessment (TA) be conducted? To begin to understand available frameworks for assessing threats, a systematic review of TA research literature was conducted. Most TA literature pertains to a specific domain (schools, public figure threats, workplaces) and target audience (clinicians, school personnel, law enforcement). TA guidelines are typically based on literature reviews with some based on empirical measures and others having no strong evidential basis. The most common concepts in TA are exploration of the threatener's mental health, the motivation for the threat and the presence of any plans. Rather than advocating for the development of a protocol for conducting TA, this article outlines the common areas of inquiry in assessing threats and highlights the limitations of current TA guidelines.

A cultural assessment of family dispute resolution: Findings about access, retention and outcomes from the evaluation of a Family Relationship Centre

The cultural appropriateness of human service processes is a major factor in determining the effectiveness of their delivery. Sensitivity to issues of culture is particularly critical in dealing with family disputes, which are generally highly emotive and require difficult decisions to be made regarding children, material assets and ongoing relationships. In this article we draw on findings from an evaluation of the Family Relationship Centre at Broadmeadows (FRCB) to offer some insights into and suggestions about managing cultural matters in the current practice of family dispute resolution (FDR) in Australia. The brief for the original research was to evaluate the cultural appropriateness of FDR services offered to culturally and linguistically diverse (CALD) communities living within the FRCB’s catchment area, specifically members of the Lebanese, Turkish and Iraqi communities. The conclusions of the evaluations were substantially positive. The work of the Centre was found to illustrate many aspects of best practice but also raised questions worthy of future exploration. The current article reports on issues of access, retention and outcomes obtained by CALD clients at various stages of the FRCB service.

A cultural assessment of family dispute resolution: Findings about cultural appropriateness from the evaluation of a family relationship centre

The cultural appropriateness of human service processes is a major factor in determining the effectiveness of their delivery. Sensitivity to issues of culture is particularly critical in dealing with family disputes, which are generally highly emotive and require difficult decisions to be made regarding children, material assets and ongoing relationships. In this article we draw on findings from an evaluation of the Family Relationship Centre at Broadmeadows (FRCB) to offer some insights into and suggestions about managing cultural matters in the current practice of family dispute resolution (FDR) in Australia. The brief for the original research was to evaluate the cultural appropriateness of FDR services offered to culturally and linguistically diverse (CALD) communities living within the FRCB’s catchment area, specifically members of the Lebanese, Turkish and Iraqi communities. The conclusions of the evaluations were substantially positive. The work of the Centre was found to illustrate many aspects of best practice but also raised questions worthy of future exploration. The current article reports on overall cultural appropriateness, particularly identifying barriers which may inhibit access and how acculturation may play a role in reducing perception of barriers. An earlier article reported on access, retention and outcomes for these CALD groups (Akin Ojelabi et al., 2011).

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

Mixed pixel analysis and assessment for flood mapping

The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated. We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results. Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.

Context-based assessment: Creating opportunities for resonance between classroom fields and societal fields

There is on-going international interest in the relationships between assessment instruments, students’ understanding of science concepts and context-based curriculum approaches. This study extends earlier research showing that students can develop connections between contexts and concepts – called fluid transitions – when studying context-based courses. We provide an in-depth investigation of one student’s experiences with multiple contextual assessment instruments that were associated with a context-based course. We analyzed the student’s responses to context-based assessment instruments to determine the extent to which contextual tests, reports of field investigations, and extended experimental investigations afforded her opportunities to make connections between contexts and concepts. A system of categorizing student responses was developed that can inform other educators when analyzing student responses to contextual assessment. We also refine the theoretical construct of fluid transitions that informed the study initially. Implications for curriculum and assessment design are provided in light of the findings.