MACHINE LEARNING TO COMBAT ILLEGAL FISHING IN COSTA RICA
Costa Rica is being assisted by OceanMind to investigate illegal fishing through new technology that works using machine learning. It is well known that one of the main economic means of Costa Rica is fishing.
However, there are a lot of illegal fishing boats, which must be controlled by the authorities, so that they do not affect the balance of fish production so it would harm the country’s economy.
OceanMind and machine learning
Although the coast guards try by all means to control illegal fishing in Costa Rica, they don’t always have the necessary means to carry out a control of all boats that are illegal, which are currently many, is the result of the studies carried out by OceanMind. The results reveal that there are approximately 100 ships that are illegal or with the intent to be.
But who is OceanMind? They are a non-profit company that is responsible for providing worldwide assistance in matters of monitoring, intelligency research, surveillance and control to the fishing authorities and the units that are in charge of supplying seafood. Likewise, they are in charge of providing support in their training.
All that they do through a series of intelligent and sophisticated mechanisms that work with machine learning and are managed by a group of expert computer analysts, who, assisted by their teams, obtain information on the behavior of ships and analyze it to determine if they represent a threat in illegal fishing.
The data received by special teams is obtained through satellites or international fishing data. So they are safe and reliable, which allows analysts to tell when illegal fishing exists with complete certainty.
The studies
In the studies that OcenaMind provided to Costa Rica, they managed to detect a large movement of threats from illegal fishing. This later allowed the authorities to continue monitoring these threats and to be able to capture them when they are about to fish in the exclusive economic zones of Costa Rica.
The monitoring that is done is accompanied by a series of vigilances made by the coast guard and patrols at all hours of the day, so that they can guarantee the safety of the coast and safeguard the nation’s resources.
Great things and important findings can be accomplished with machine learning monitoring the oceans, as long as the data they yield is analyzed correctly by fisheries experts.
The OceanMind organization uses machine learning because this is a type of artificial intelligence that is applied to computers and different types of equipment so that they learn continuously and independently, without the need for any type of control.
That is why their monitoring is so effective. They register certain data and patterns, learn from them and the next time they find it easier to find their targets, which in this case is illegal fishing in Costa Rica.
The use of machine learning is increasingly used because of the great advantages it presents, so it should not only be limited to the ocean as used by OceanMind.
There are currently some other uses of artificial intelligence that are being applied to aid and improve other areas in culture and nature.
Examples
One prime example of ML (machine learning) is an algorithm with the ability to predict aerospace or weather behaviour. It works in such a way that it obtains data from space, analyzes it and is able to predict future phenomena, giving experts a space of time to act or prepare in some way.
ML especially has predictive rather than explanatory capacity, this means that when you analyze the data you can predict but this hinders your ability to give answers to the predictions it makes. It is something that happens very often with automatic learning systems, when it has the function of predicting, but hardly explains and vice versa.
The neural networks that ML uses have the main objectives of taking the data and assembling structures and patterns, resulting in graphic representations that can be observed and analyzed.
Some areas in which ML can be used are: the ecological analysis of landscapes, where the data is analyzed and results can be given from the interaction that exists between objects; It is also applied to crop analysis, where it is able to predict when it could be ready.
Thanks to its Geospatial ML system, you can map the areas, providing data that allows you to predict accidents and avoid them.
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