Yield Consortium: agriculture and remote sensing from space

In collaboration with BASF Digital Farming, John Deere, and Munich Re, DFKI develops predictive models for selected arable crops in the focus regions of Europe, South and North America. Later models will extend to other relevant crops and growing regions.

Environmental challenges in our world today are materializing on a global scale, in the form of food crises, wars, and the consequences of a changing climate. The farming industry needs transparency and assistance to react to changing environments and to successfully execute crisis management in the farming sector. The German Research Center for Artificial Intelligence (DFKI) launched the Yield Consortium project, funded through the ESA InCubed Programme to make an important contribution towards research. The consortium is part of the AI4EO Solution Factory, a collaborative research effort by ESA and DFKI, which seeks to find new, real world solutions for business and industry on the basis of satellite data.

The benefit of the Yield Consortium project is the ability to proactively react to the impending changes in agricultural yields. Satellite data is used at an early stage to reliably predict expected yields. The industry partners support the modelling process and share their complementary expertise in the areas of finance, agronomy, and harvesting systems.

Yield predictions will enable new insurance calculation concepts in terms of irrigation, fertilizers, crop protection, and profit planning that result in better loss estimates in the future. These forecasts are not only beneficial for industry and authorities, but also for the farmers themselves. They are useful in optimizing cultivation methods and improving crop protection strategies.

In just a short time, the interdisciplinary team has managed to develop and successfully test an end-to-end model, which meets the requirements of the industry partners. Currently, yield forecasts can be determined for wheat and rapeseed crops in Germany and for soybeans in South America, Argentina, and Uruguay. This is achieved by using satellite data as well as soil properties, plant growth stages, weather information, and digital elevation models.

Yield forecasts are recorded at different points in time. For example, agricultural yields can be determined at harvest time and up to 120 days prior to the harvest. The closer the date of interest is to the harvest, the more precise the forecast will be. The answers to foundational questions in the area of logistics and grain distribution are now more easily available: What location is best suited for individual crop? What kind of yields can we expect this season? Which herbicides/pesticides and how much fertilizer should be applied? Yield optimization is significantly dependent on the answers to all of these questions.


"Our goal is to provide forecasts of agricultural yields as accurately as possible. This should help farmers with their day-to-day decisions. We would like to gradually expand the model to other countries, take more crops into account, and improve the existing models. It is our goal to develop a model that can make global predictions for all major crops," said Dr Marlon Nuske of the AI4EO Solution Factory.

DFKI researcher Dr Michaela Vollmer describes a practical application: "As it harvests a field, the combine continuously measures the yield in tons per hectare with high resolution such that it can be attributed to a point of origin in the field. The measured harvest points or 'geo locations' allow us to see at the sub-field level, the point-by-point yield for the field. We use this high-resolution data to match the images from space with a 10x10 meter pixel resolution. This is an advantage as it allows optimal training of our machine learning model. In turn, the farmer is provided with better information to apply tailored actions for each field."

"We are still looking for more cooperation partners who can provide high-quality yield maps.“

The team is interested in acquiring additional yield data to further develop the model. "We have already begun successful collaborations with the agriculture-related companies Smartway and Manexa to supply yield data. However, we are still looking for more cooperation partners who can provide high-quality yield maps," said Dr Marlon Nuske.

"The success of the Yield Consortium will help us to attract additional industry partners from different subject areas for research, implementation, and practical model development.“

"The success of the Yield Consortium will help us to attract additional industry partners from different subject areas for research, implementation, and practical model development. The combination of AI and EO in the project expands beyond basic research. The ongoing exchange with industry partners at DFKI facilitates knowledge transfer to different application areas via so-called TransferLabs. We look forward to collaborating with additional partners from other industrial sectors," added Professor Andreas Dengel, Managing Director, DFKI Kaiserslautern and head of the Smart Data & Knowledge Services research area.

Global food security and the agricultural sector are already facing global challenges. No other sector is so dependent on climatic factors and so directly affected by climate change: loss of arable land, changing temperatures, weather events such as heavy rainfall, heat waves, and forest fires in the wake of global climate change are just some of the most serious phenomena. It is true that extreme weather events have always existed. Yet, recent years have seen a rapid increase in the frequency of climate induced events. The continuous release of greenhouse gases is affecting the cultivation of corn, wheat, rice and other crops. Traditional farming is built on many years of experience of which crop to plant and where conditions are most favorable for it. Continuing to implement this in the future will be difficult without having timely assistance through risk mitigation and seasonal management that responds to shifting and unpredictable yields.



Present macrosocial challenges are becoming much more pronounced with regard to global food security. The agricultural sector is facing serious repercussions from a world food crisis triggered by the imminent shortfalls and threats from war zones of preventing grain exports. Sound data about agricultural yields can help to better assess these troubling scenarios. The AI models in the Yield Consortium project are designed to provide reliable yield forecasts and facilitate seasonal decision-making.