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Seed industry

40,000+ employees

15,000+ MEUR

Project overview

Global deployment of vision AI for seed breeding and seed production monitoring

Each year, BASF conducts several thousands of research trials in agricultural stations worldwide to measure product performance under different field conditions.

Collecting such a high quantity of data means that there is a need for streamlined backend solutions so that BASF can realize the full potential of their visual drone data.

Partnering with Alteia, BASF's agricultural research stations use the Alteia platform to streamline and standardize sensor-based field studies data. This allows them to turn visual drone data into actionable insights and, ultimately, new sustainable solutions for the agricultural market.

For example, thoroughly understanding the observed crops, their surroundings, and how they respond to environmental conditions can reduce the time to market for new products. In addition, working on a single cloud platform allows field agronomists to automatically vectorize and geo-reference microplots and generate biological data and crop behavior per plot.


reduction of R&D cycles
countries under supervision
savings on data processing
Each year, BASF conducts several thousands of research trials in agricultural stations throughout the globe to measure product performance under different field conditions. As a research driven agricultural company, we want to use the full potential of digitalization to accelerate innovation. Partnering with Alteia will help us to get a deeper understanding of the observed crops and their surrounding environments, and reduce the time to market for new products.

Greta De Both
Manager of Sensor-based Field Phenotyping for Seeds & Traits

Project highlights

Scale up research and development projects for seeds, traits and crop protection

Automated and end-to-end data flow

Automate your recurrent data collection and analysis operations thanks to the integrated season planner module. With this specific feature you can automate data processing chains  by describing the sequence of analyses to be performed throughout the season; standardize processing chains between different sites; track progress of data collection and processing tasks.

It offers a multitude of advantages.

Firstly, it enhances efficiency by reducing the need for manual data handling, which minimizes the risk of errors and speeds up data analysis.

It also ensures scalability, as it can handle large volumes of data without a proportional increase in human resources.

Support decisions for field trial validation based on plant density/stand counts

Plant density is a key piece of information about crop growth that guides decision-making, informs agricultural strategies, and ensures sustainable and productive farming practices, making it an indispensable component of field trial analysis.

Firstly, it serves as a fundamental indicator of crop health and productivity.

Furthermore, this data is invaluable for assessing the impact of environmental factors, such as weather and soil conditions, on plant growth, enabling better adaptation to changing climate patterns.

Automatize and standardize crop trait measurement process

Automating crop trait measurement offers a multitude of benefits that revolutionize agriculture and research.

It reduces human error, ensuring precise and consistent measurements, which are crucial for the accuracy of research findings and breeding programs.

Moreover, automated systems can capture a wider range of traits, including those that are difficult to assess manually, leading to a more comprehensive understanding of crop performance.

Evaluate quickly and consistently plant response to crop protection products

By continuously monitoring crop growth and health over time, researchers can track changes in vegetation indices and other key indicators. This data can then be analyzed in a time series format, allowing for the observation of trends and patterns in crop behavior before and after the application of protection products.

Such analysis can reveal the effectiveness of these products in terms of pest or disease management, as well as their impact on crop growth and overall health.

Additionally, time series data enables the identification of optimal application timings and dosage levels, contributing to more efficient and sustainable agricultural practices.

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