Mark Dunn

Behavioural mechanisms affecting energy regulation in mice prone or resistant to diet-induced obesity

We investigated inbred SWR/J and AKR/J mice, two established models for different susceptibility to diet-induced obesity (DIO), to scrutinize the contribution of physical activity and energy assimilation to the etiology of developing obesity. Body mass gain and body composition of mice fed a high-energy (HE) or a low caloric control diet were monitored. In parallel, assimilated energy, locomotor activity and thermoregulatory behaviour were measured. Activity was continuously registered by radio telemetry and, in addition, Open Field (OF) behaviour was used as a quick screening tool for spontaneous activity before and after the feeding trial. Energy assimilation was increased in both strains on HE (AKR/J: +60.7% and SWR/J: +42.8%) but only in AKR/J, body mass (+8.1%) and fat mass (+40.7%) were significantly elevated. As a trend, total home cage activity was increased and was more scattered in SWR/J. Interestingly, HE stimulated OF activity only in SWR/J in the second trial at the end of the feeding experiment. The spatial pattern of OF activity also differed between strains with obese mice avoiding the core area. Under housing conditions, nest building behaviour was more pronounced in AKR/J. To further evaluate OF behaviour as a marker for spontaneous activity an obese mouse line was investigated. Mice lacking the leptin receptor (db/db) showed already before the onset of obesity lowest activity levels in OF. Adjustment of energy intake, higher activity levels and energy consuming thermoregulatory behaviour are mechanisms employed by SWR/J mice to dissipate excess energy as a defence against the onset of obesity. Therefore our results deciphering mechanisms of DIO-sensitivity in mice contribute to the understanding of inter-individual differences in body weight development in an adipogenic environment.

Using machine vision classification to control access of animals to water

Invasive vertebrate pests together with overabundant native species cause significant economic and environmental damage in the Australian rangelands. Access to artificial watering points, created for the pastoral industry, has been a major factor in the spread and survival of these pests. Existing methods of controlling watering points are mechanical and cannot discriminate between target species. This paper describes an intelligent system of controlling watering points based on machine vision technology. Initial test results clearly demonstrate proof of concept for machine vision in this application. These initial experiments were carried out as part of a 3-year project using machine vision software to manage all large vertebrates in the Australian rangelands. Concurrent work is testing the use of automated gates and innovative laneway and enclosure design. The system will have application in any habitat throughout the world where a resource is limited and can be enclosed for the management of livestock or wildlife.

Unusual vision – machine vision applications at the NCEA

Purpose – The paper draws together a range of somewhat unusual machine vision applications, with an integrating overview.Design/methodology/approach – The National Centre for Engineering in Agriculture is amassing a portfolio of projects involving machine vision. These include identification of animal species, visual counting of macadamia nuts, analysis of animal behaviour and a number of quality control functions. DirectX tools have been developed and are applicable across a wide range of applications, while theory is extended in several ways.Findings – Most of the projects are still in progress, but results are reported on the degree of success of a range of methods. Strategies and algorithms are discussed.Research limitations/implications – Vision‐based solutions are applied to a diversity of tasks. There will be a continuing stream of such problems with abundant opportunities for research.Practical implications – The projects are in essence practical, although they have inspired new methodologies. The...

Vision based macadamia yield assessment

Purpose – To describe the prototype macadamia nut yield monitor.Design/methodology/approach – In this paper, the machine vision‐based yield monitor for macadamia nut plantations is described. A summary of sensor fusion procedures is presented. Additionally, a summary of current testing progress is provided.Findings – Using vision to count nuts has the potential to revolutionise yield monitoring for the macadamia industry. Additionally, using a vision sensor for in‐field location can provide a low cost, highly accurate method of positioning. Tractor (and nut) location can be determined accurate to 12 mm.Practical implications – This project has culminated in the creation of a working prototype harvester. A commercial unit is in the design stage for operation in 2007 harvest season.Originality/value – This paper describes the solution to a particular problem in the macadamia industry, with potential use in wider fields.

The use of machine vision for assessment of fodder quality

At present fodder is assessed subjectively. The evaluation depends greatly on a personal opinion and there can be large variations in assessments. The project has investigated the use of machine vision in several ways, to provide measures of fodder quality that will be objective and independent of the assessor. Growers will be able to quote a quality measure that buyers can trust. The research includes the possibility of discerning colour differences that are beyond the capability of the human eye, while still using equipment that is of relatively modest cost.

Animal management in the Australian rangelands

Invasive vertebrates and overabundant native species cause significant economic and environmental damage in the Australian rangelands, which cover more than 70% of the continent. Some estimates put the annual loss in livestock production due to such pest species at more than

Machine vision system for counting macadamia nuts

700 million.1–3 Access to pastoral watering points has been a major factor in the spread and survival of pest species. While methods of controlling access watering points have been developed,4 they are problematic. Essentially all current trap systems rely on oneway barriers and cannot discriminate between pest and desired species. To address this problem, we have developed a system of identifying animal species using machine vision, which uses computers to process images and then applies the resultant information to manipulate equipment. In recent years such systems have becomemore common in remote-sensing applications for all aspects of the agricultural domain5 as well as many areas of wildlife management.6 When used in conjunction with an enclosure and an automated gate, our system can control animals’ access to a limiting resource, like water,7 and thus function to exclude pest species, trap desired species, and otherwise manage livestock. It requires no physical contact with the animals, reducing both system maintenance requirements and animal stress. Computing capabilities have now developed to the point that processing of real-time video data is also feasible. The concept that governs the system is simple: control the water (or other resource), and you control all large vertebrates (>10 kg) that require water to survive. Three main components achieve this goal. First, an enclosure with an entry lane surrounds the limiting resource. For the machine vision software to accurately identify animals to the species level, it must view the animals laterally and in single-file arrangement. Our enclosure and entry lane were constructed of galvanized weld mesh Figure 1. A watering point enclosure excludes some species while allowing access to others. The gate remains open, closing when undesirable animals are detected in the entry lane, then reopening after a predetermined period of time.