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Water and Agriculture

Artificial intelligence

Data-driven agriculture benefits from technologies such as artificial intelligence (AI), which enables prediction and agile response to unforeseen events. AI analyzes data from various sources, such as satellite images, to provide farmers with accurate, real-time information.

The implementation of guidance and sensing technologies, analytics, and other technologies have enabled data-driven agriculture. AI (artificial intelligence) has made it possible to predict and react quickly to unpredictable agricultural situations. Predictive analysis helps maintain efficient agricultural production and is one of the most widespread AI use cases in agriculture. The data used by these AI systems can come from different sources. One of them is through the analysis of satellite images with machine learning algorithms. With the help of new technologies and artificial intelligence, farmers gain full control over the entire plantation. Software that correctly analyzes the data can warn about problems in real time and make the necessary and appropriate decisions to make crops as profitable as possible. AI in agriculture consists of aerial and aquatic drones, electrical, visual, olfactory, and biological sensors, with all possible information it creates analysis files of the area, depending on what is needed to know, combining information from geographic position systems and genetics.

Water use efficiency

Main theme:

Central

Region:

2,500 - 2,700

Precipitation (mm):

Average

Application difficulty:

1, 2, 12 and 13

SDGs impacted:

Electric

Energy used:

70 - 90

Efficiency (%):

Urban/Rural

Sector:

It optimizes the processes of water production, storage and distribution, thereby reducing consumption of the resource.

Expected environmental impact:

Free options

Estimated value:

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