Artificial intelligence in agriculture is a tool that looks like something out of science fiction, but in reality is being implemented for the benefit of worldwide producers, especially for those who work with precision farming.
In this type of agriculture they use a set of technologies applied to the field, in order to gather the necessary information for decision-making that the farmer must anticipate. This is how they determine what to plant, where, when, and can even predict the volume of their crops.
For Hence, it relies on artificial intelligence (AI), machines perform tasks that can be technical or the imitation of inductive and deductive processes of human thought. To achieve this, scientists rely on electronic circuits and sophisticated computer programs, which are fed with data to electronically copy the functioning of the brain.
This type of ‘learning’ is a method of computing in which programmers do not place a specific function, but train the computer to recognize patterns. For example, they learn the behavior of healthy and diseased leaves to determine where to spray an herbicide and where not to. Thanks to these algorithms, machines can also determine when it is an outbreak and when it is a weed.
Artificial intelligence in agriculture, key to productivity
AI only works if it is applied on specialized machines that fulfill specific functions and are programmed to fulfill a previously established objective. In agriculture, one of the scopes with the greatest potential is the analysis of information from abroad, that is, knowing how crops develop in their environment and, with this information, making predictions.
The data to apply AI in agriculture is usually taken by means of sensors, drones or tractors, and then suggest to the farmers the actions they must carry out throughout their agricultural year. An example of this is taking into account the way in which the rains have behaved in different periods and, based on that, choosing an irrigation method or even a change in crop type.
How it will affect crops
Crops require water retained in the soil to carry out physiological and biological processes. This is known as water requirements.
This demand for resources varies depending on the crop, environmental conditions, land management and the growth phase in which it is found. To solve it, there are cultivation guides, but these guides only include general suggestions for the preparation of the land and do not analyze locally the needs of each producer, that is why the use of applications that work with the specific information of each one is so relevant.
These technologies could especially benefit regions where the problem is accentuated by the high variability of the rains and the dependence of farmers on storm practices, a type of agriculture that depends on the behavior of the rain during production cycles and the ability of the soil to capture and conserve moisture. The uncertainty caused by these practices are a burden for producers, who are affected by shortages of rain, delays, hail and even drought, since the only source of water for their seasonal crops is precipitation.
In addition to this, FAO anticipates that climate change will affect agricultural practices, becoming a risk to food security and to the work of a large part of the population worldwide. Thus, promoting innovations of this type is an urgent activity to maintain and even increase agricultural productivity.
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