Precision Agriculture with Databricks

Turning tractor sensor data into predictive yield intelligence.

Client Background

Hexagon is an industry leader in geospatial and agricultural technology. Their systems power smart farming solutions across multiple regions, helping farmers improve accuracy, productivity, and sustainability.

Hexagon wanted to enhance their agricultural platform by adding advanced data and predictive capabilities using Databricks, allowing tractor sensor data to turn into actionable yield predictions.

The Problem: Raw Tractor Data Was Hard to Use for Real Insights

Farming equipment generates huge volumes of data, including:

Soil moisture and nutrient levels
Tractor speed
Engine load
Slope and terrain movement
Fuel usage
Implement performance
GPS and location points
Weather patterns

But the challenges were clear:

Data came from multiple sensors and formats: Each tractor and tool produced different types of data, often incomplete or noisy.
No unified platform: Sensor data lived across local systems, files, and disconnected scripts.
Hard to build predictive models: Without clean and structured data, yield prediction models could not operate reliably.
No scalable way to analyse field performance: Farmers needed simple, fast reports not raw CSVs or logs.
Manual steps slowed down insights: Data often had to be processed by hand before analysts could use it.

Hexagon needed a platform that could collect, clean, analyze, and predict, all in one system.

Solution Overview

Tenplus designed and delivered a complete Databricks-based data platform for precision agriculture that includes:

Automated data ingestion
Sensor data processing
Medallion architecture (Bronze → Silver → Gold)
Predictive modelling
Analytics-ready reporting views

The system supports both current and future tractor sensors, allowing new data sources to be added easily.

Data Analysts

Our in-house data analysts have a hands-on approach to data.

Companies analyzed

Last year, we were able to help nearly 50 companies.

Process

Data designers

Our designers have an extensive background in data analysis.

Visualizations made

From presentations to marketing materials and beyond.

Ongoing projects

Data changes, but our team stands by your side when you need us.

Results: A Modern Agriculture Intelligence Platform

With Tenplus and Databricks, Hexagon achieved:

Real-time understanding of field performance: Farmers receive insights as tractors operate.
Accurate yield predictions: Predictive models help plan better and reduce risk.
Clean, unified sensor data: All tractor inputs are structured and easy to analyse.
Faster decision making: Operators no longer need to manually process data.
Better use of fuel, equipment, and resources: Field-level insights support efficiency and sustainability.
Scalable architecture for global operations: The solution is ready for multiple regions and tractor fleets.

This platform enables true precision agriculture powered by modern data engineering.

⭐⭐⭐⭐⭐
“Tenplus helped us bring advanced data and AI capabilities into our agriculture products. Databricks gave us the perfect platform for sensor analytics and yield prediction. The solution is fast, scalable, and adds real value for our farming customers.”

Engineering Manager, Hexagon Agriculture

We’ve Helped Them

We’ve partnered with many household brand names to deliver insights and solutions to their problems with big data.

Happy Clients

Don’t just take our word for it – our clients frequently stay in touch with us and work with us on future projects that require big data insights.

I work in agricultural consulting, meaning that we often need to use big and complex datasets to justify expensive investments for our clients. DataAI consultants helped us see the big picture with their visualization tools and expertise.

As the in-house supplier manager, it's sometimes had to understand which manufacturers work best for our needs. DataAI's data analysis helped us identify where we could save and which manufacturers worked the best for us in the long term.

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