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Field of Research : Computer Vision
Research Topic : Tropical Fruit
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Computer Vision (3)
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Berry Fruit (excl. Kiwifruit) (2)
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Harvesting and Packing of Plant Products not elsewhere classified (1)
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  • Funded Activity

    Linkage Projects - Grant ID: LP0560847

    Funder
    Australian Research Council
    Funding Amount
    $72,444.00
    Summary
    Fruit shape estimation from stereoscopic images in real time. The research aims at improving the process of automatic fruit inspection and classification. Existing stereo vision algorithms to extract depth information are unsuitable for real time calculations. The increasing complexity and reducing cost of field programmable gate arrays along with the development of algorithms that have a high degree of parallelism and locality has created the possibility of performing the calculation .... Fruit shape estimation from stereoscopic images in real time. The research aims at improving the process of automatic fruit inspection and classification. Existing stereo vision algorithms to extract depth information are unsuitable for real time calculations. The increasing complexity and reducing cost of field programmable gate arrays along with the development of algorithms that have a high degree of parallelism and locality has created the possibility of performing the calculations required in real time. This projects aims to investigate the suitability of the various stereo vision algorithms available in the literature for real time hardware implementation with application to fruit shape estimation it real time.
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    Funded Activity

    Linkage Projects - Grant ID: LP150100658

    Funder
    Australian Research Council
    Funding Amount
    $406,000.00
    Summary
    Precise recognition for automated harvesting and grading of strawberries. This project aims to improve automated strawberry harvesting to enable industrial harvesters to be deployed for commercial use and to lift the productivity of the Australian fruit industry. Precise recognition and grading of strawberries is a major obstacle in developing fully-automated commercial strawberry harvesting systems. Current colour-based fruit recognition techniques have intrinsic limitations in meeting the need .... Precise recognition for automated harvesting and grading of strawberries. This project aims to improve automated strawberry harvesting to enable industrial harvesters to be deployed for commercial use and to lift the productivity of the Australian fruit industry. Precise recognition and grading of strawberries is a major obstacle in developing fully-automated commercial strawberry harvesting systems. Current colour-based fruit recognition techniques have intrinsic limitations in meeting the needs of automatic strawberry harvesting. This project aims to investigate high-level syntactic recognition approaches that embed high-order texture patterns of ripe fruit and hyperspectral analysis techniques to achieve partially occluded fruit recognition and grading of fruit at the level required by commercial production.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210200213

    Funder
    Australian Research Council
    Funding Amount
    $540,000.00
    Summary
    Precision Pollination: Data-driven enhancements to boost crop yield. The project aims to transform industrial crop pollination from an intuitive domain to one where decisions are based on sound data and best-practice principles. It proposes to achieve this modernisation of global pollination practice by developing novel technologies to operate a three-stage loop: honeybee pollination monitoring, simulation-based forecasting, and management. This is intended to ensure that the capability of honey .... Precision Pollination: Data-driven enhancements to boost crop yield. The project aims to transform industrial crop pollination from an intuitive domain to one where decisions are based on sound data and best-practice principles. It proposes to achieve this modernisation of global pollination practice by developing novel technologies to operate a three-stage loop: honeybee pollination monitoring, simulation-based forecasting, and management. This is intended to ensure that the capability of honeybees to provide essential ecosystem services is informed by transferable, standardised data acquisition and management techniques that maintain bee health and maximise pollination. The anticipated outcomes are higher fruit yields and quality, and a beneficial step-change in industry productivity and profitability.
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