A world without Artificial Intelligence (AI) is a world with slow, bureaucratic processes without automation and precision in choices. It is a world of uncertainty and accumulated problems. Fortunately, today’s world has evolved to eliminate these obstacles and incorporate AI into every possible professional segment. Artificial Intelligence in agribusiness is already a reality.
The evolution of technologies in this sector provides more accuracy, fewer errors and greater control over the continuity of operations. Thus, companies can increase productivity and speed up the response to unforeseen events and failures. This is digital transformation! Want to know more? Continue reading this article!
Challenges Faced By The Agribusiness
Agribusiness is a complex sector which involves a series of nuances and challenges. One of the issues is predicting the natural, meteorological conditions for planting, cultivation and care with productions. Even if we have predictive ability, a degree of uncertainty still makes processes unsafe.
The presence of pests in planting is another major obstacle to be overcome. This is a phenomenon that management cannot predict with 100% accuracy and, in many cases, is not able to combat it. Waste consumption of water and resources is another challenge.
We can also mention the environmental impact of the actions. This motivates a specific resistance from sectors of society about agribusiness activity. Thus, a demand was created for more sustainable strategies that protect the environment.
How Is Artificial Intelligence Transforming Agribusiness?
AI is not a new technology. It’s a concept that has been stealing the show for decades. The idea is experiencing a new height lately due to the confluence with other technologies, such as the Internet of Things (IoT) and cloud computing. Due to this power, there are applications in different sectors, such as agribusiness.
Artificial Intelligence works on data. Upon receiving a large mass of semi-structured or unstructured data, the system can identify patterns and trends in this information. It can perform various calculations to understand how the data are related and predict a new insight by a movement or an indicated projection.
Within the concept of Artificial Intelligence in agribusiness, we have several nuances, such as Machine Learning and Deep Learning.
Machine Learning is data-driven learning and training with large masses of big data. Deep Learning corresponds to advanced Machine Learning, which abstracts certain parts of the data analysis and generates even more accurate results.
Image Problem Detection
There is a subfield of AI called computer vision. Its focus is on recognizing images and making inferences based on what is learned about them. In the agribusiness sector, it is possible to use systems to analyze photos of farming regions, searching for diseases, pests or problems related to foreign elements.
This can reinforce monitoring through wireless sensors. In this case, AI works together with the Internet of Things, receiving the IoT’s generated data and interpreting it in real-time.
With the new intelligent tools, it becomes feasible to study aspects of temperature, precipitation, the occurrence of climatic phenomena and others to determine the incidence of rain. This predictability contributes to precision in planting care to avoid wrong decisions.
With the support of sensor monitoring, it is possible to perform more intelligent and predictive maintenance. The repair can be done even before the equipment presents a problem to avoid stops and crises in operations. This transparency and ability to anticipate problems generates cost savings and enables process continuity, which directly impacts productivity.
Another exciting innovation is the presence of autonomous cars. This application saves personnel costs and avoids problems with tractors, harvesters, and mobile equipment. There is more control of vehicle conditions, fuel management and internal aspects. There are improvements in agility in use as well, as factors such as distraction and fatigue are eliminated.
It is generally possible to perform a complete mapping of properties to generate more visibility and avoid unexpected events. Management can identify regions that need irrigation, deforested areas and other vital issues. Mapping allows the complete study with AI to suggest insights.
Optimization Of Areas
Artificial intelligence makes the decision-making process more accessible as it is based on factual data. It is possible, for example, to know which areas are most suitable for certain plantations and the cultivation of certain foods.
Demand And Price Forecast
Another point is the ability to predict demand and prices. With this facility, the company can know when the order is higher for certain products and how it can price its cultivated items according to that demand. Pricing projections are based on various factors, including the varying prices charged in the market.
As we have seen, the applications of Artificial Intelligence in agribusiness are highly beneficial. Companies become more productive, agile, efficient, and cost-effective with AI innovations.