Deep machine learning prevent salmon escapes in choppy waters

Perhaps you’ve already heard about Deep machine learning or deep learning. It is a crucial step in the machine learning process, allowing computers to grow in capability in response to information given to them. A computer brain can be taught to identify contextual information by introducing it to concepts that it needs to comprehend. Devices are connected to create what is known as a neural network, a data structure that is based on the human brain. Such networks are composed of numerous layers that interact to make deep learning possible.

This idea has been used to create and enhance systems that can detect anomalies in salmon cage nets used in fish farms. We accomplish this by putting photographs of the components of an intact net into a digital neural network. The algorithm can then respond when it notices something unusual, like a hole, thanks to these photographs. This is Deep machine learning.

To help stop fish from escaping, sea cage nets that are suspended underwater must frequently be inspected. In addition to potentially carrying diseases, escaped farmed salmon have been known to move upstream along rivers with wild fish, disrupting and taking part in spawning. It is in everyone’s best interest to avoid escapes from sea cages because these factors only contribute to reduce wild salmon populations.

Recently, the Norwegian government issued notice of its intention to enact stronger technical specifications for the escape-proof construction and management of fish farms. These new rules clearly state that we must think creatively, and they may present us with opportunity to put some of the ideas we are currently exploring at the SFI Exposed research center to use or develop further.

A remotely operated vehicle (ROV) with a camera installed is currently used as part of normal operations. The ROV is launched into a cage and commanded by an operator, who then reviews the images broadcast back. An operator will find it challenging to focus for long periods of time when watching a repetitive series of video images of underwater nets. On the other hand, a computer brain never gets weary or bored, making such activities perfect for the use of autonomous vehicles that use picture recognition.

The kinds of sensor technology that can allow a ROV to detect its spatial position within a marine cage have been investigated by our research partners who work with autonomous systems and technologies for usage in distant areas. Such details are essential for determining which area of the net is being examined at any particular time.

For all forms of autonomous activities, a ROV must be able to pinpoint its location. It might have to maintain its position in the face of strong currents and rough seas, or it might have to follow a predefined path over the net wall.

A laser camera system for so-called net-relative navigation is the end product of all this study. Two parallel laser beams that illuminate the net wall are used to measure the distance and angle of the ROV with respect to the net. If the ROV is to maintain a proper distance from the net wall without requiring a steering change each time the net moves owing to currents or wave action, such readings are crucial. Autonomous robotic operations are the subject of a lot of fascinating research and development, both in academic and commercial settings. We at SINTEF regard this as the start of a path toward system development that will improve aquaculture industry operations.

In the scenarios robot arm is better than human arm

In order to assess the dangers and difficulties involved, we first looked at how crane operations are currently conducted. Next, we looked at how emerging technology might make things better. Our concept studies for the ROV sea cage “Launch and Recovery” are one example of Deep machine learning. Another idea involves using a sophisticated robot arm to perform tasks without making any touch at all between the vessel and the cage.

Imagine that while the sea cage is far away and out of sync with the vessel that the robot arm is installed on, both are going up and down on the waves. A robot arm that can span from the vessel to the cage and perform precise work must unquestionably be an outstanding piece of high-tech equipment. However, that is actually totally feasible.

In order to create a reliable statistical database, SINTEF Ocean has been developing an infrastructure for data collection from buoys, offshore vessels, and facilities. We have also gathered meteorological data, measures of water flow, and measurements of water quality. It will be simpler to decide whether or not to carry out an operation if we have more specific information on wave, current, and weather conditions. These statistics form a strong basis for operational planning when combined with physical measures and digital twins.

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