Technology is reshaping the world, and the agricultural sector is no exception. Artificial intelligence (AI) and predictive analytics are at the forefront of this transformation, providing actionable insights, valuable resources that enable more efficient and sustainable management. E-commerce is becoming an increasingly important component of Brazilian agriculture. It enables an additional sales channel and relationship between chain participants. At the same time, e-commerce facilitates data collection and analysis, which can improve the accuracy of demand forecasting.
Brazil is at the forefront of technology research and development. We are undergoing the transition from Agriculture 4.0, which focuses on machinery and technological solutions, to Agriculture 5.0. This new phase incorporates robotics, machine learning and AI to agricultural production systems, with a focus on productivity and sustainability.
Predictive analysis
AI, with its ability to process and analyze large volumes of data, is being used to identify patterns and relationships that were previously difficult to detect. This is especially useful in agriculture, where factors such as climate, soil, and cultivation practices can significantly impact production. As a subfield of AI, predictive analytics uses historical data and machine learning algorithms to anticipate future demand and optimize production and distribution.
Agtechs
According to Embrapa (Brazilian Agricultural Research Corporation), more than 2,000 Brazilian agtechs (startups dedicated to agribusiness) are boosting the sector with IoT (Internet of Things) and AI tools. Furthermore, the value of AI investment in the global agricultural market, according to Statista, is expected to grow to approximately US$1.4 billion by 2028. This represents a promising transformation for the sector.
Challenges
The successful implementation of technologies in agribusiness faces some challenges related to the collection and analysis of large volumes of data, in addition to the need to develop appropriate machine learning algorithms and ensure data security.
Still, they should shape the evolution of agribusiness, helping companies not only with demand forecasting but also with supply chain optimization and operational efficiency. Furthermore, they can help promote sustainability by reducing waste and improving food safety and quality.
