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Application of novel machine learning techniques and high speed 3D vision algorithms for real time detection of fruit

Application of novel machine learning techniques and high speed 3D vision algorithms for real time detection of fruit
应用新颖的机器学习技术和高速 3D 视觉算法进行水果实时检测
批准号:
2150214
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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英文摘要
This project addresses automated detection of fruit, which is an essential requirement for developing robotics for fruit picking, focusing on strawberry. It aligns closely with the following pillars of the industrial strategy: (1) investing in science, research and innovation, and (2) developing skills. If successful, this methodology could be generalised to other fruit crops, however by focussing on strawberry we are addressing one of top priorities in research and development of the UK fruit industry. The industry is extremely concerned that the impact of Brexit may threaten the availability of migrant workers to pick fruit crops. Furthermore, it is under significant price and cost pressure. Notwithstanding Brexit, the longer-term demographics of age will reduce the availability of on farm labour. However irrespective of these issues, the consumer will still want an on-going and likely increasing supply of fresh nutritious fruit. This is driving the industry to seek new means to drive overall productivity. Novel digital technologies including vision systems, robotics and autonomous systems are seen as potential game changers for the sector. Visions systems can be used to assess and sense the crop to enable better decision support; robotics and autonomous systems offer new means to drive productivity. These issues apply to all soft and top fruits, but also more widely across the whole fresh produce sector. However, all picking and vision systems are dependent on the development of complex algorithms developed to identify, measure and locate fruit in real time. The development of these systems is not trivial, especially in outdoor environments where the back ground light level and quality can change within an instant. Here we will deploy novel machine learning technologies to detect, locate and measure (size and colour) fruit in real time. This work fundamentally underpins the development of all crop-picking robots.
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