The numbers that define the moment
The Radar Agtech Brasil 2025, prepared by Embrapa in partnership with SP Ventures and Homo Ludens and presented at the Radar Agtech Summit 2026 in São Paulo, registered 2,075 startups focused on agribusiness in Brazil — a 5% growth over the previous year. The most revealing number, however, is not the total number of companies: it is that 83% of Brazilian agtechs already use artificial intelligence as a central component of their solutions, according to a PwC survey in partnership with the same report.
Globally, the AI market in agribusiness jumped from $2.71 billion in 2025 to $3.37 billion in 2026, with a compound annual growth rate of 24.5%, according to Bem Agro data based on consolidated market projections. Brazil is the main regional center for agtech development: the Radar Agtech LAC survey mapped 2,656 startups in 23 countries in Latin America and the Caribbean, with Brazil accounting for more than 78% of the total.
What "tropicalizing AI" means in practice
Brazilian agribusiness has a complexity that no other country in the world replicates: diversity of biomes (Cerrado, Amazon, Pantanal, Atlantic Forest, Pampa, Caatinga), variety of crops (soy, corn, coffee, orange, sugarcane, eucalyptus, extensive and intensive livestock), territorial breadth (850 million hectares of mapped arable land), and growing pressure for traceability in demanding export markets.
This means that imported AI — trained on European or American production data — underperforms in the Brazilian context. As the Agroadvance 2026 analysis summarizes: "Brazil has consolidated its position as the largest agricultural laboratory on the planet. In our tropical environment, technology cannot be merely imported; it must be tropicalized or, preferably, developed here."
It is this context that explains why Brazil has 2,075 agtechs — more than all of Latin America combined except Brazil itself — and why this ecosystem continues to grow even with the "startup winter" that reduced the pace of global growth.
The three AI fronts delivering results
Computer vision for crop monitoring: Drones equipped with multispectral cameras and computer vision models can map a 1,000-hectare farm in a few hours, identifying vegetation index (NDVI) variations, pest outbreaks, water stress areas, and planting failures with centimeter accuracy. What used to take weeks of manual inspection is now delivered in 24-48 hours with automated analysis. Brazilian startups such as Horus Aeronaves and Agrosmart lead this segment. EMBRAPA, with its AgroIA project, has democratized some of this technology for small producers via smartphones — the app allows leaf pest diagnosis with the cell phone camera, with 91% accuracy for the top 40 soy pests.
Predictive crop and weather models: Precision agriculture has evolved from simple variability maps to integrated systems that process data in real time. Models that cross historical production data, soil conditions, weather forecasts, and input prices can project yield per plot weeks in advance — enabling more precise management decisions and more robust financial planning. The Weather Company (IBM) and Climate Corporation (Bayer) have the most advanced models for Brazil. In the national segment, Aegro and FarmHQ offer integrated agricultural management solutions with predictive components. Incomplete connectivity in remote areas is addressed with edge computing and offline-first solutions — a problem that EMBRAPA's AgroIA also had to solve.
Prescriptive AI in the agricultural supply chain and logistics: 2026 reports point out that prescriptive AI and autonomous agents are already a reality on large and medium-sized properties, especially in the Cerrado and Matopiba regions. Systems that automatically recommend fertilizer doses by management zone, optimize harvest routes, and coordinate transport logistics based on port demand forecasts are reducing logistics costs — historically one of the biggest bottlenecks for the competitiveness of Brazilian agribusiness.
The agtech ecosystem: who is leading
Radar Agtech 2025 shows that "inside the gate" agtechs — focused on solutions applied directly in the field — continue to be the majority, with 852 companies. This group has more than doubled since 2019. "After the gate" startups total 841 companies (logistics, traceability, commercialization), and "before the gate" ones (inputs, biopesticides, biotechnology) total 382.
The Radar Agtech LAC survey positioned Brazil as a regional benchmark: Argentina has 158 agtechs, Mexico 110, Chile 91. The consolidation of the Brazilian ecosystem reflects the "natural selection" of the post-startup winter: "Startups more connected to the reality of the producer tend to reach greater maturity, because they need to deliver real value. Otherwise, there is no adoption by the producer," stated researcher Favarin during the Summit.
What is still missing: the real bottlenecks
Rural connectivity: 71.7% of Brazilian schools have internet (2026 data), but farm connectivity is much lower — especially in the North and Center-West regions where agricultural production is most intense. Edge computing and hybrid cloud solutions partially compensate but increase the cost and complexity of deployment.
Adoption by the small producer: Brazilian agribusiness is dual: large producers in the Cerrado adopt AI quickly; small family farmers — who account for 77% of Brazil's agricultural establishments according to the Agricultural Census — have very limited access to the same tools. EMBRAPA's AgroIA is one of the few initiatives directly addressing this gap.
Proprietary data and interoperability: Each agricultural management platform accumulates proprietary data. The lack of interoperability standards between systems creates data silos that limit the potential for more robust AI models. Open data initiatives for agribusiness are still nascent in Brazil.
Agribusiness as an applied AI laboratory
Brazilian agribusiness is becoming one of the world's main laboratories for AI applied to adverse conditions: extreme heat, low connectivity, climatic and cultural diversity, and real economic pressure. The models developed for the Brazilian field have export potential to other tropical and subtropical countries — a technological soft power opportunity that Brazil has not yet systematically explored.
