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About

Robotics Agriculture

Agriculture is quickly becoming an exciting high-tech industry, drawing new professionals, new companies and new investors. The technology is developing rapidly, not only advancing the production capabilities of farmers but also advancing robotics and automation technology as we know it.

Agricultural robots are increasing production yields for farmers in various ways. From drones to autonomous tractors to robotic arms, the technology is being deployed in creative and innovative applications.

Agricultural Robot Applications

Agricultural robots automate slow, repetitive and dull tasks for farmers, allowing them to focus more on improving overall production yields. Some of the most common robots in agriculture are used for:

  • Harvesting and picking
  • Weed control
  • Autonomous mowing, pruning, seeding, spraying and thinning
  • Phenotyping
  • Sorting and packing
  • Utility platforms

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ROBOTIC
PLATFORMS

Agricultural platforms can be divided into domain- and task-specific robots designed to perform a specific task on a given crop in a pre-defined domain, and generic platforms designed to perform several tasks in different

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SENSING AND
PERCEPTION

The integration of sensor systems within autonomous robotic systems offers the significant potential for new measurements that would otherwise be unobtainable.

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PLANNING AND
COORDINATION

The true potential of robotics in agriculture will be harnessed when different types of robots and autonomous systems are brought together in a systemic approach.

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MANIPULATION

Automated manipulation and grasping of food items presents a series of unique challenges compared to other sectors. These include significant natural size and shape variations between examples of the same product, heterogeneous positioning of products (e.g. during harvesting) and the fragile nature of food products.

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HUMAN-ROBOT
INTERACTION

The challenges for interaction range from domainindependent aspects such as intuitive designs, immersive displays (e.g. Virtual and Augmented Reality) and tactile feedback, to very specific challenges stemming from the in-field conditions.

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LEARNING AND
ADAPTATION

Artificial intelligence technologies, especially in machine learning, are expected to play a major role in most of the above technology areas, and will be essential enablers for agricultural robots. Agricultural environments are subject to changes throughout the lifetime of a robotic system.

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Agriculture & AI
Robots

Thus the agricultural industry is in transition. And that transition differs country by country, state by state, region by region as well as by type of farming practiced: from primitive to conventional to precision to experimental. A little bit of everything is going on everywhere but the general trend worldwide is toward precision agriculture supplemented by advanced technologies including robotics.

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Growing Industry

Robotics and Autonomous Systems (RAS) are set to transform global industries. These technologies will have greatest impact on large sectors of the economy with relatively low productivity such as Agri-Food (food production from the farm through to and including manufacturing to the retail shelf). The UK Agri-Food chain, from primary farming through to retail, generates over £108bn p.a., with 3.7m employees in a truly international industry yielding £20bn of exports in 2016.

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ECONOMIC AND SOCIETAL FACTORS

Robotics and Autonomous Systems (RAS) are set to transform many global industries. These technologies will have greatest impact on large sectors of the economy with relatively low productivity such as Agri-Food (food production from the farm through to and including manufacturing to the retail shelf).

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ENVIRONMENTAL BENEFITS

As well as delivering economic benefits, such as increasing productivity and reducing waste throughout the food supply chain, developing a new focus for RAS within Agri-Food will have significant societal and environmental benefits.

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PRECISION AGRICULTURE

Also known as ‘smart farming’, precision agriculture has its origins in developments first applied in industrial manufacturing as far back as the 1970s and 80s. It concerns the use of monitoring and intervention techniques to improve efficiency, realised in application through the deployment of sensing technologies and automation.

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Conceptualising Robitic Agriculture with His Highness

MINISTER OF Science and Agriculture - UGANDA

Finalizing Project for Robitic Agriculture with His Highness

VICE PRESIDENT - UGANDA

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