Applications of artificial intelligence in agriculture
Posted: Mon Jan 20, 2025 8:59 am
Artificial intelligence in agriculture is a strong ally for sustainable, faster and more profitable production. Innovative technologies can offer more expressive cultivation and harvesting results, using the same amount of land.
But what are, in fact, the applications of artificial intelligence to achieve this goal? How to organize to use fewer machines and reduce fuel consumption? How to avoid waste when using inputs? Take a look at the ways to make this a reality below.
What are the applications of artificial intelligence in agriculture?
Data culture is a reality in virtually every industry. However, data processing within the field is too complex to do without the help of an efficient system.
Through networks installed in the field, it is possible to monitor, in real time, everything from mechanized operations to the amount of rain that reaches each sector. It is also possible to track production automatically. See below the points that most feel the positive impact of technology in agriculture .
Crop monitoring
One of the greatest assets of digital agriculture is the possibility of inspecting plantations, using images captured by drones and data from sensors spread across the field. The system can accurately assess the presence of pests.
Automatic traps consist of an application. Their sensors help the azerbaijan whatsapp data producer to know when the application of an agricultural defense is necessary. This fight must be done at the right time and in specific places, controlling pests without high costs and with less impact on the environment.
Other types of threats can also be analysed. Machine learning techniques are able to cross-reference information and patterns, identifying, for example, that a part of the crop has a low amount of nutrients in the soil or a lack of water. This is well before it can be noticed with the naked eye.
Weather forecast
Instead of relying solely on weather forecasts for the region in which they work, farmers now have access to predictions for temperature, wind, solar incidence and rainfall. The system is based on data from local properties. This precision of information generates greater reliability and gives more security to the producer.
It is much easier to know exactly which part of the crop needs to be reinforced with irrigation or the use of fertilizers, a type of technique common in precision agriculture.
Autonomous vehicles
When it comes to agricultural machinery, there are already prototypes of telematic tractors, i.e. tractors that are self-driving. But AI artificial intelligence will take this automation to the next level.
These vehicles will be able to decide to interrupt their activities in case of very heavy rain, for example, changing their route and heading to a more suitable location. The process is monitored remotely by the producer and his collaborators via smartphone.
With the ability to identify threats based on patterns analyzed in a gigantic database, an unmanned ground vehicle is also capable of accurately dosing the amount of pesticide on a crop, without any direct human interference.
Along the same lines, smart harvesters of perennial crops, such as coffee or oranges, can move around the plantation and identify ripe fruit, also discarding those that have already rotted or show some kind of irregularity.
But what are, in fact, the applications of artificial intelligence to achieve this goal? How to organize to use fewer machines and reduce fuel consumption? How to avoid waste when using inputs? Take a look at the ways to make this a reality below.
What are the applications of artificial intelligence in agriculture?
Data culture is a reality in virtually every industry. However, data processing within the field is too complex to do without the help of an efficient system.
Through networks installed in the field, it is possible to monitor, in real time, everything from mechanized operations to the amount of rain that reaches each sector. It is also possible to track production automatically. See below the points that most feel the positive impact of technology in agriculture .
Crop monitoring
One of the greatest assets of digital agriculture is the possibility of inspecting plantations, using images captured by drones and data from sensors spread across the field. The system can accurately assess the presence of pests.
Automatic traps consist of an application. Their sensors help the azerbaijan whatsapp data producer to know when the application of an agricultural defense is necessary. This fight must be done at the right time and in specific places, controlling pests without high costs and with less impact on the environment.
Other types of threats can also be analysed. Machine learning techniques are able to cross-reference information and patterns, identifying, for example, that a part of the crop has a low amount of nutrients in the soil or a lack of water. This is well before it can be noticed with the naked eye.
Weather forecast
Instead of relying solely on weather forecasts for the region in which they work, farmers now have access to predictions for temperature, wind, solar incidence and rainfall. The system is based on data from local properties. This precision of information generates greater reliability and gives more security to the producer.
It is much easier to know exactly which part of the crop needs to be reinforced with irrigation or the use of fertilizers, a type of technique common in precision agriculture.
Autonomous vehicles
When it comes to agricultural machinery, there are already prototypes of telematic tractors, i.e. tractors that are self-driving. But AI artificial intelligence will take this automation to the next level.
These vehicles will be able to decide to interrupt their activities in case of very heavy rain, for example, changing their route and heading to a more suitable location. The process is monitored remotely by the producer and his collaborators via smartphone.
With the ability to identify threats based on patterns analyzed in a gigantic database, an unmanned ground vehicle is also capable of accurately dosing the amount of pesticide on a crop, without any direct human interference.
Along the same lines, smart harvesters of perennial crops, such as coffee or oranges, can move around the plantation and identify ripe fruit, also discarding those that have already rotted or show some kind of irregularity.