AI and data in agribusiness: efficiency, predictability, and competitiveness in the field.

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AI-generated summary:

The use of AI and data is transforming agribusiness at every stage of the chain, from crop planning to marketing. Technologies such as Machine Learning and GenAI allow for more accurate demand forecasting, real-time crop monitoring, reduced input costs, increased productivity, and meeting growing sustainability demands. Real-world cases in the United States and Latin America demonstrate concrete gains in operational efficiency and competitiveness. With over 200 AI and data projects delivered to more than 90 clients, BlueMetrics combines data expertise with a robust portfolio of proven solutions, ensuring agile implementations and measurable short-term results for producers and managers in the sector.

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Agribusiness has always been a strategic sector for the Brazilian economy, responsible for a large part of the GDP and global leadership in several production chains. But in recent years, the pace of change has accelerated. Climate change, pressure for sustainability, a shortage of skilled labor, rising input costs, and the need to integrate marketing channels have created a scenario in which "doing things the way they've always been done" is no longer sufficient.

It is in this context that AI and data emerge as crucial allies for increasing efficiency, reducing risks, and improving competitiveness. More than just dashboards and static reports, modern solutions combine Machine Learning and GenAI to anticipate scenarios, simulate impacts, and support real-time decision-making.

From field to market: where algorithms make the difference.

Managers of medium and large agribusiness ventures are dealing with increasing complexity. Producing more and better remains the goal, but now it needs to be reconciled with tight margins, regulatory requirements, and new customer and consumer expectations. In this environment, the use of AI and data ceases to be a trend and becomes a competitive differentiator.

A clear example is in crop forecasting. Machine Learning models can cross-reference historical production data with external variables, such as climate, soil quality, rainfall levels, and even futures market information, to project productivity with much greater accuracy. This allows for planning input purchases, hiring, and even marketing strategies before the harvest even begins.

Another fertile field for application is the management of climate and phytosanitary risks. AI algorithms are able to detect patterns that indicate the risk of pests or diseases before they spread, using satellite imagery, drones, and field sensors. Climate forecasting models, when combined with historical analyses, help define the best planting or harvesting window, reducing losses.

Productivity and sustainability as two sides of the same coin.

One of the great challenges of modern agribusiness is producing more with less environmental impact. Here, the application of AI and data generates direct gains. Intelligent systems automatically adjust irrigation based on soil moisture and weather forecasts, avoiding water waste. Fertilizers and pesticides can be applied in a localized way, only where they are needed, reducing costs and increasing the sustainability of the operation.

This movement aligns with the growing pressure from investors, international buyers, and regulatory bodies, who demand more transparent and sustainable supply chains. The use of GenAI can even support the generation of compliance reports and the consolidation of environmental, social, and governance (ESG) indicators, simplifying a process that was previously expensive and bureaucratic.

From planning to marketing

While the gains are evident within the farm gate, opportunities exist outside it as well. The consumer market is increasingly digital and demanding. Platforms supported by GenAI help producers and cooperatives personalize offers for different channels, generate pricing insights, and simulate marketing scenarios.

For large agribusiness groups involved in exporting, the use of AI also allows them to cross-reference variables such as exchange rates, international demand, and logistics, offering an integrated view to define the best sales strategy.

More than just increasing efficiency, the integration of AI and data in agribusiness represents the possibility of transforming management: less dependent on intuition and more guided by evidence, anticipation, and intelligence.

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From concept to practice: where AI and data are already transforming agribusiness.

In the first part, we conceptually explored how AI and data, in the form of technologies like Machine Learning and GenAI, can support agribusiness in various ways. Now, it's time to look at concrete experiences. Several companies and institutions are already applying these technologies in their daily work in the field, achieving productivity gains, cost reductions, and greater predictability. These examples serve as inspiration for managers of medium and large enterprises who wish to accelerate their digital journey without relying solely on intuition or retrospective analysis.

1. Case Study: FarmWise Labs (United States): Weed control using computer vision and robotics.

Problem

In vegetable gardens and diversified plantations, weed management would require the extensive use of herbicides or manual intervention, both of which are expensive, environmentally impactful, and imprecise. In many cases, herbicides are applied indiscriminately, even where there is no infestation, leading to chemical waste and unnecessary costs.

Solution

The company FarmWise Labs has developed automated mechanical weeding robots that use AI, computer vision, and robotics to identify and remove weeds in vegetable crops. The Titan FT-35 robot, for example, visually analyzes the soil and crops to distinguish weeds from cultivated plants, removing unwanted soil without affecting the main crops.

They offer this service charging per acre treated, which allows the farmer to outsource weeding using state-of-the-art technology, without needing to purchase equipment or have a high level of operational expertise.

Observed results:

• Reduced use of chemical herbicides, as intervention becomes localized instead of widespread application.

• Lower operational costs related to manual weeding or maintenance of weeds after excessive chemical application.

• Lower environmental impact, with less soil and water contamination, in addition to contributing to more sustainable agricultural practices. Although there are no publications quantifying all the gains in broad percentages in the public domain, the business model has already been recognized as an innovation in efficiency and sustainability.

2. Case IH (LatAm): precision agriculture, connectivity and self-management

Problem

In large-scale agricultural operations, machines, tractors, harvesters, and sprayers operate under variable conditions: terrain, soil type, weather conditions, and performance vary according to use. Without reliable real-time data or intelligent automation, operational efficiency falls short of ideal, in addition to costs associated with maintenance, fuel consumption, and human error.

Solution

Case IH has unveiled a digital ecosystem for precision agriculture that incorporates connectivity, digitization, and machine learning to enable managers to remotely monitor and optimize operations, and allow machines to self-adapt. Some details:

• Machines equipped with multiple sensors (temperature, humidity, operating conditions) that allow real-time monitoring.

• Systems that automatically adjust operating modes (e.g., harvesting), self-regulating machine parameters based on terrain/sensor data to maximize yield or reduce wear.

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Observed results

• Increased operational productivity on machines, with less rework and less unproductive time.

• Fuel economy, less machine wear and tear (more effective preventive maintenance).

• Improved resource efficiency, including reduced environmental impact.

3. AgroTIC Case Study (Colombia): Smartphone + ML use for producers

Problem

Smaller or medium-sized producers in areas like Santander (Colombia) have limited access to agronomists and technical advice, little real-time visibility into crop health, and difficulties connecting to markets efficiently.

Solution

• A smartphone-based application that allows for crop health monitoring with the assistance of agronomists. It uses deep learning/ML techniques to process images captured by the producer, identifying diseases or deficiencies in plants.

• A platform that connects producers with buyers/markets, helping with the sales process.

  
  

Observed results

• Improved crop quality, as problems are identified earlier.

• Higher yields and reduced losses due to plant health problems.

• Better product valuation in the market, due to quality.

4. Case Omdena (United States): mapping of agroforestry systems via satellite + AI

Problem

Agroforestry systems are productive systems that combine trees with agricultural crops, benefiting biodiversity, sequestering carbon, and improving the soil. However, many of these systems are not accurately mapped, which hinders planning, monitoring, verification of carbon credits, and decision-making. Without mapping, there is low visibility into the exact location, extent, and characteristics of these systems, making it difficult to implement policies, incentives, and land-use management.

Solution

Omdena's "Mapping of Agroforestry Systems based on Artificial Intelligence" project uses satellite imagery with AI algorithms to automatically identify agroforestry areas, distinguish types of vegetation cover, and delineate these areas on georeferenced maps. This allows institutions, producers, and governments to visualize where agroforestry systems exist, their size, and their composition, with much greater granularity than traditional inventories.

Observed results

• Improved spatial visibility of agroforestry systems, with detailed maps that allow for the identification of areas that can be restored or that are already operating within the system, with greater precision than manual surveys.

• These maps could be used for carbon credit programs, incentive policies, or sustainability monitoring.

• Accurate detection allows for faster forest or agroforestry planning, facilitates decision-making, and supports stakeholders (producers, regulators, buyers) with reliable data.

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How can BlueMetrics apply these learnings?

These cases clearly show that, for medium and large-scale agricultural operations, the intelligent combination of Machine Learning, AI, and data produces real gains: productivity, cost reduction, better use of inputs, operational efficiency, and sustainability.

At BlueMetrics, with over 200 AI and data projects for more than 90 clients in the United States, Brazil, and Latin America, we have a vast repertoire to adapt these solutions to your business. Here are some areas of our expertise that guarantee accuracy and speed in achieving results:

• Data quality : We know that any AI model depends on clean, well-structured, and integrated data. We have experience in diagnosing, cleaning, enriching, and integrating diverse databases (satellite, sensors, ERP, logistics).

• Choosing the right technology : not every project needs GenAI; but when we combine GenAI with Machine Learning and automation, we create more robust solutions. For example, recommendation systems for input, or assistants that help managers simulate scenarios.

• Well-defined and scalable pilot projects : we typically start with a field or crop, or a key operation (e.g., pest monitoring, crop forecasting, logistics routing), show results in the short term, and then scale up.

• Concrete and measurable deliverables in the short term : because customers expect a return. With real benchmarks like the ones above, with established deadlines and goals (for example, a reduction in pesticide use of X%, an improvement in yield of Y%, or operational cost savings of Z%), it is possible to demonstrate value quickly.

Conclusion: It's time to transform data into results in the field.

Brazilian agribusiness has already proven to be a global economic engine, but the future demands a new layer of intelligence. AI and data are now key to meeting the challenges of productivity, sustainability, and competitiveness, transforming the sector into something even more efficient and resilient.

For managers of medium and large enterprises, the time to act is now. The solutions already exist, case studies prove their effectiveness, and the competitive edge will come from those who can integrate technology, data, and strategy in a practical and fast way.

At BlueMetrics, we believe in AI and data solutions for the real world: projects that don't stay in the lab, but deliver value in the field, in operations, and in the bottom line. The next step could be yours. Let's talk about it?

Learn about some Use Cases .