Application Of A Machine Learning Approach For Effective Stock Management Of Farmed Abalone
Funder
Fisheries Research and Development Corporation
Funding Amount
$115,649.00
Summary
Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals (as it is removed from the water) which can compromise growth and survival. ....Determining the number and size distribution of abalone present at various stages of production is critical information for effective stock management. Currently the Australian abalone aquaculture industry spends in the order of $25,000 per annum, per farm, gathering this information by hand. However, the resulting data is of mediocre quality, is limited in its scope, and collecting the data causes stress to the animals (as it is removed from the water) which can compromise growth and survival. Automated counting and measuring of abalone will increase farm efficiency and productivity in the short term and, in the longer term, will provide an advanced platform for further R & D improvements including accurate data collection during experimental trials (e.g. feeds, temperature). Artificial intelligence and machine learning has now matured to a point that accurately counting and measuring abalone is possible using this approach, however specific application to the abalone industry is yet to be achieved. This project would involve the development, training and validation of a machine learning model to identify, segment and measure quantitative abalone traits in production systems and, render the product data to be accessible and applicable for farmers. Objectives: 1. To develop and implement artificial intelligence as a method for accurately measuring and counting abalone at nursery, weaning and grow out. Read moreRead less
Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era. In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering ap ....Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era. In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering applications which will both reduced the bandwidth required and make the internet safer for users. Finally, it will enable rapid deployment of sensor networks for monitoring and detection, such as for environmental monitoring and safeguarding Australia's borders.Read moreRead less
Investigate Oceanographic And Environmental Factors Impacting On The ETBF
Funder
Fisheries Research and Development Corporation
Funding Amount
$500,000.00
Summary
As specified in the FRDC call for proposals, there is a need for AFMA, its advisory committees and the ETBF industry to gain a much stronger understanding of past, current and potential future oceanographic and environmental impacts upon (i) the spatial and temporal distribution and level of ETBF catches, catch rates, fishing effort and fish sizes (particularly those indicators used in the ETBF harvest strategy), and (ii) the interactions between focal species in the ETBF with domestic (e.g. rec ....As specified in the FRDC call for proposals, there is a need for AFMA, its advisory committees and the ETBF industry to gain a much stronger understanding of past, current and potential future oceanographic and environmental impacts upon (i) the spatial and temporal distribution and level of ETBF catches, catch rates, fishing effort and fish sizes (particularly those indicators used in the ETBF harvest strategy), and (ii) the interactions between focal species in the ETBF with domestic (e.g. recreational) and international fisheries. We have established relationships with regional partners, and pending endorsement, which will allow comprehensive collation of catch and tracking data for the focal species, such that habitat models for the whole region can be developed. This will permit hypotheses about movement of fish cohorts into the Australian region, and movements of these fish within the Australian EEZ to be tested.
This proposed research is needed to ensure the effectiveness (note, the ETBF already has a developed HS) and further development of appropriate management arrangements, including harvest strategies and resource sharing arrangements. It will complement current genetic research into stock structure and connectivity, with implications for harvest strategies and potentially Australia’s position on key management issues and approaches being considered or developed in the Western and Central Pacific Fisheries Commission (WCPFC). By collating data from the countries in the south-west Pacific Ocean and New Zealand regions, we will seek to understand patterns in regional abundance. Importantly, this project will provide insights into potential long term changes in the ETBF that may result from climate change, and deliver forecasting capability on seasonal and decadal time scales. We will identify the influence of any large scale oceanographic drivers on availability of these key species in Australian waters, such as the strength of the East Australia Current, or the teleconnections resulting from ENSO events.
Objectives: 1. Enhance AFMA and industry understanding of influence of climate-ocean system drivers upon the spatial and temporal variability of key ETBF species. 2. Develop and deliver predictive models at seasonal and decadal time scales to assist management and industry planning 3. Provide operational forecasts of habitat distribution for Australia and the regional partners within the life of the project 4. Inform harvest and allocation discussions at national and international scales Read moreRead less
Reconceiving Machine Learning. The proposed research will develop a new way to consider problems to which machine learning can be applied. Machine learning is crucial enabler of the digital economy. The research will provide better opportunities for Australian industry to gain a competitive advantage with machine learning technology. The framework developed will enable better opportunities for collaborative research and will build and strengthen international linkages.
Exploiting Structure in AI Planning. The research will improve our ability to build generic, automated planning systems, which can efficiently select effective courses of actions in a range of situations such as crisis management, project planning, military operations planning, and transportation. It will help reduce the cost of building software to more efficiently solve important problems occurring in validating, controlling, and diagnosing complex systems. More generally, it will advance our ....Exploiting Structure in AI Planning. The research will improve our ability to build generic, automated planning systems, which can efficiently select effective courses of actions in a range of situations such as crisis management, project planning, military operations planning, and transportation. It will help reduce the cost of building software to more efficiently solve important problems occurring in validating, controlling, and diagnosing complex systems. More generally, it will advance our understanding of how machines can intelligently solve complex problems by identifying and exploiting their relevant structure.Read moreRead less
Foundations and Architectures for Agent Systems. Computer systems are now involved in many aspects of everyday life, commerce, and industry. Making these systems more intelligent has thus become a priority research issue. Agents systems, with their emphasis on autonomy, proactiveness, reactivity, and sociability, are widely regarded as a crucial technology for realising the capabilities that computer systems will need over the next few decades. The proposed research aims to make some fundamenta ....Foundations and Architectures for Agent Systems. Computer systems are now involved in many aspects of everyday life, commerce, and industry. Making these systems more intelligent has thus become a priority research issue. Agents systems, with their emphasis on autonomy, proactiveness, reactivity, and sociability, are widely regarded as a crucial technology for realising the capabilities that computer systems will need over the next few decades. The proposed research aims to make some fundamental contributions to agent systems that will be used to build future computer systems that will have an even more profound positive impact on everyday life, commerce and industry than existing systems.Read moreRead less
Engineering Artificial Intelligence: A Spatial Representation and Reasoning Perspective. Spatial information is important in areas of national interest such as mining and exploration, environmental monitoring and planning, emergency response, and defence. Mission control centres, for instance, receive different forms of spatial data from satellites, radar, or people on the ground. They have to process the input data and make intelligent decisions in a very limited time. Intelligent systems that ....Engineering Artificial Intelligence: A Spatial Representation and Reasoning Perspective. Spatial information is important in areas of national interest such as mining and exploration, environmental monitoring and planning, emergency response, and defence. Mission control centres, for instance, receive different forms of spatial data from satellites, radar, or people on the ground. They have to process the input data and make intelligent decisions in a very limited time. Intelligent systems that are able to assist with processing different forms of spatial data efficiently and that offer reliable decision support are essential for improving the quality and reliability of such applications. This research enables future intelligent systems with these capabilities. This will directly benefit applications in areas of national interest.Read moreRead less
There is a need to assess new technologies in collecting and assessing fish health data. New technologies offer the potential to increase sampling, speed up basic assessment, improve basic diagnostic accuracy, lower costs and possibly limit the need for pathology to when it is really needed. It also provides the opportunity to improve public reporting of fish health issues. While the original proposal by Infofish was unsuccessful we were invited to submit a revised proposal that focused on bette ....There is a need to assess new technologies in collecting and assessing fish health data. New technologies offer the potential to increase sampling, speed up basic assessment, improve basic diagnostic accuracy, lower costs and possibly limit the need for pathology to when it is really needed. It also provides the opportunity to improve public reporting of fish health issues. While the original proposal by Infofish was unsuccessful we were invited to submit a revised proposal that focused on better automated data collection (Trackmyfish) and assessment (Machine Learning) to test the application of these technologies.
Objectives: 1. To deploy tools to automate data collection and assessment of fish health using data collected in Gladstone Harbour as a trial. 2. To undertake structured data collection of fish samples using Gladstone Healthy Harbour Partnership’s sub-regions and the Boyne Tannum HookUp fishing competition. 3. To evaluate the potential to adapt the methods developed to monitor fish health in other estuaries and ports in Australia. Read moreRead less
Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior ....Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior knowledge of physical environments, etc.) and combine the advantages of feature based high-level vision methods with low-level machine learning techniques.
This proposal is part of a joint IST project with partners from the European Union.Read moreRead less