Artificial Intelligence applied to agriculture: what is it?
What is Artificial Intelligence?
Artificial intelligence is the ability of machines to learn to perform complex tasks, understanding the context in which they are performed, and this allows them in some cases to make decisions based on the knowledge acquired. It has existed for many years and has been progressing. It is a tool that has and will have many capabilities that are being discovered over the years as the technology continues to be used.
Nowadays, artificial intelligence is a reality in the global agricultural sector and is being applied on a large scale by leading companies and innovators.
How is AI applied in agriculture?
Agriculture is a major economic activity in many countries, including Argentina. Factors such as climate change, population growth and food security are driving the agro industry to seek innovative approaches to improve crop yields. Artificial intelligence plays a crucial role in the technological evolution of the agro-industry.
It refers to the use of technology to optimize crops. This improves human capabilities and assists in solving problems. The results allow farmers to profitably invest in their crops. Precision agriculture is possible with the use of sensors, signalers, drones, and other technological advances.
In the field, there are as many applications as there are crops, from applications for detecting early disease and assessing damage to weed control to robots for harvesting fruits and vegetables, to satellite data analysis, to agronomic resource management and livestock monitoring. An extensive range of predictive models to improve agronomic decision making, detection of nutritional deficiencies in crops, and other relevant applications in this innovative ecosystem.
Below we will refer specifically to four technological advances in artificial intelligence in agriculture that successful farmers use:
Machine learning is a form of artificial intelligence that is used in multiple domains. The goal of the program is to develop the ability for computers to learn and improve continuously.
Its agricultural applications are focused on algorithms that can assist producers across a wide range of fields.
Big Data can be very useful for agriculture professionals, who can access cloud-based information. Depending on their needs, they handle large amounts of data and images to work with.
Deep Learning is a subtype of Machine Learning. This structured automatic algorithm enables layered learning from image recognition. In agriculture, it is applied to process information.
Robots are autonomous machines capable of performing specific tasks in the field. In Argentina, they perform activities such as:
- Monitoring: drones and ground robots.
- Control: equipment for geo-referenced applications of phytosanitary products.
- Harvesting: equipment for harvesting fruit in perfect conditions.
Finally, we can say that artificial intelligence in agriculture is a reality in the modern era. It seems that the implementation of this technology will increase exponentially, as it will allow agriculture to double its production, encouraging a more profitable business.
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