"The potential of AI and IoT in agriculture is immense."

As the global agricultural sector grapples with challenges like climate change and feeding an ever-growing population, innovative solutions are essential.

Dr. Sebastian Bosse is working at the Fraunhofer Heinrich Hertz Institute in Berlin on a project in collaboration with the FAO to increase efficiency and sustainability in agriculture worldwide through the use of artificial intelligence and the Internet of Things.

Last year you took over the leadership of the newly founded ITU focus group "AI and IoT for Digital Agriculture". What goals and challenges do you see with regard to the digitalization of agriculture at international level and how do you plan to address them?

The potential of AI and IoT in agriculture is immense. Let's start with the clear benefits these technologies can bring. Through IoT sensors, farmers can collect data in real time, be it on soil moisture, weather conditions or plant health. This provides an informed basis for decision-making. Artificial intelligence (AI) goes one step further, analyzing these data sets to make precise recommendations, be it for the optimal sowing time, the perfect amount of irrigation or disease prevention.

Translating this to a global scale, the goal of digitization in international agriculture becomes clear: to ensure food security while conserving resources and the environment. With a rising global population and limited agricultural resources, AI and IoT could help increase the efficiency of food production while minimizing environmental impact.

But implementing these technologies is not without challenges. Interoperability and standards play a crucial role here. In a globally connected world, systems and technologies must be able to communicate with each other. This means that a sensor manufactured in Germany should be just as compatible with a system in Kenya or Brazil. Standards ensure that there is no patchwork of technologies that inhibits the adoption and scaling of these solutions.

Another critical issue at the international level is technology access. While developed countries can more easily access advanced technologies, developing countries could fall behind. This could lead to a technology imbalance that undermines global food security efforts.

There are also issues of data privacy and security. If farmers collect and share data globally, who has access to it? And how is it ensured that this data is not misused?

Finally, cultural and social aspects need to be taken into account. What is seen as a beneficial technology in one country might be met with resistance in another due to traditional farming practices or social norms.

In summary, AI and IoT have the potential to fundamentally transform agriculture, especially on an international scale. However, successful integration requires careful consideration of interoperability, standards and the specific challenges posed by the global context.

How could this look concretely on the ground?

The integration of AI and IoT in agriculture on the ground can take many forms and vary depending on the specific requirements and circumstances of each region. Here is a concrete scenario of how it could be implemented in a community or on a farm:

Imagine a farm that has been using traditional methods. The first step would be to install IoT sensors throughout the field. These sensors could monitor soil moisture, temperature, light intensity and other important parameters. This data is then transmitted in real time to a central database or cloud platform.

A specialized AI system analyses this data continuously. It can predict when would be the best time to irrigate or fertilize, or it can give early warning of changing conditions, such as an impending disease outbreak or pest infestation. This allows farmers to be proactive and use resources more efficiently.

Drones could also be used. Equipped with cameras and other sensors, they could fly over large areas to monitor the condition of crops. AI models could analyze the images collected by the drones to detect signs of disease or pests that may have been missed or are not visible to the naked eye.

Smart tractors or autonomous machines could be programmed to perform specific tasks such as ploughing, sowing or harvesting, based on the AI's data and predictions. These machines could also communicate with other systems to ensure optimal efficiency.

In terms of interoperability and standards, it would be important that all these devices and systems can communicate seamlessly with each other. This means that sensors, machines, drones and the central data platform can work according to the same standards and exchange information without obstacles.

For local society, this would mean that agriculture would become more precise, efficient and productive. Smallholder farmers would be able to increase their yields, reduce the use of chemicals and minimize their environmental impact through the use of these technologies.

Of course, such a shift would not be without challenges. Investment in technology, training for farmers and adaptation to new working methods would be required. But with the right support and resources, the benefits to society and the environment could be significant.

At Fraunhofer HHI you are working on the "NaLamKI" project, which focuses on the use of AI for sustainable crop and arable farming. Could you give us examples of intelligent method development along the agricultural process chain that you are involved in?

The NaLamKI project focuses precisely on interoperability, data exchange and user-friendly provision of artificial intelligence. In principle, you can think of it as a combination of cloud storage and an AI app store. The methods we are developing address a wealth of different use cases:

In robot-based inspection in fruit growing, an autonomous robot navigates through apple orchards. Equipped with cameras and other sensors, this robot systematically inspects the fruit. Using AI models, it identifies the ripeness of the apples, their size and checks for signs of diseases or pests. The robot sends this data in real time to a central platform so that farmers can take immediate action, for example to plan a specific harvest strategy or to treat diseased areas.

In drone-based detection of yellow rust, a drone with specialized sensors and cameras flies over wheat fields. The cameras continuously record images that are analyzed by an AI system. This system is specially trained to detect the characteristic yellow rust spots on wheat leaves. In the event of an infestation, the drone sends delocalized data so that the farmers know exactly which parts of their field are affected and can treat them specifically.

In terms of camera-based weed detection, special ground vehicles or tractors are equipped with camera systems that scan the ground as they drive through it. The AI models in these systems are trained to distinguish weeds from crops. Once weeds are detected, the system can target and precisely treat them minimally with herbicides or remove them mechanically.

For satellite-based growth forecasting, images are used from satellites that take pictures of the earth's surface at regular intervals. The AI analyses these images to identify patterns in plant growth, soil moisture and other relevant factors. Based on these analyses, the AI creates precise growth forecasts. This gives farmers important insights into the expected growth of their crops and allows them to make informed decisions about irrigation, fertilization or harvest timing.

The digitalization of agriculture requires reliable data availability. How will the NaLamKI project contribute to meeting these requirements and what are the benefits for the different actors along the value chain?

The NaLamKI project enables farmers to combine their data with AI methods. We place great emphasis on user-friendliness in order to make the methods available to as many users as possible.

Understanding data in agriculture requires recognizing its inherent importance as a tool for information gathering and decision-making. It represents an economic asset because, when used correctly, it can increase yields, save resources and ultimately influence the financial success of an agricultural business.

Artificial intelligence intensifies this value because it has the ability not only to process these volumes of data, but also to create patterns, correlations and predictions from them that would often be too complex for the human mind. In this context, AI represents the mechanism, so to speak, that unleashes the potential of this data and transforms it into so-called "actionable insights".

Training plays a central role in this context. After all, an AI is only as good as the data it has been trained with. Without high-quality training, the AI might recognize wrong patterns, make inaccurate predictions or miss important signals. Training AI with data from agriculture makes it possible to develop specific models tailored to the needs and challenges of this sector. For example, a well-trained model can distinguish between different plant diseases, predict the best time for sowing or accurately estimate the water requirements of crops.

Thus, data is not only the raw material that informs an agricultural company's operations, but also the essential resource for training and refining AI systems. And in this synergy between data as an economic asset and AI lies the key to the future of digital agriculture.

NaLamKI, as a platform-based approach, creates the prerequisite for this.

To what extent does the "NaLamKI" project support the achievement of sustainability goals in the agricultural sector? What impact do you expect on the environment and resource use?

The "NaLamKI" project, which stands for "Sustainable Agriculture with Artificial Intelligence", symbolises a significant advancement in the agricultural sector. By using a platform approach that combines data and AI, it offers a new dimension to agricultural practice that has profound positive impacts on sustainability.

By integrating AI into the farming process, farmers can make more accurate decisions based on real, up-to-date data. This reduces guesswork and uncertainty in many aspects of agriculture. For example, an AI-powered irrigation system can accurately determine the water needs of crops and thus prevent water waste.

At the same time, the combination of real-time data and AI can help minimise the use of pesticides and fertilisers. By accurately identifying and locating pests or diseases, farmers can apply targeted treatments instead of spraying entire fields. This not only reduces environmental impact, but also costs.

In terms of resource use, NaLamKI will help farmers to make the best use of their resources. For example, through the use of AI-based forecasting models, crop yields can be predicted more accurately. This helps farmers to better plan their storage capacities and to make the supply chain more efficient.

The environmental impact of NaLamKI will be significant improvements in biodiversity conservation, reduction of greenhouse gas emissions and conservation of water resources. If farmers know the exact needs of their plants and can respond accurately, agriculture as a whole will become more environmentally friendly and less invasive.

In summary, NaLamKI is sustainable in two senses: the potential is to sustainably change the way farming is done by putting sustainability at its core while increasing efficiency and productivity.

Is NaLamKI an approach that is only suitable for Germany or Europe?

NaLamKI has the advantage of being highly adaptive. Next year, we will take the approach to India, adapt it to the local context and evaluate it. In this way, we will also make a concrete contribution to the transformation towards sustainable and climate-resilient food systems there.

Implementing the NaLamKI concept in India brings an exciting perspective due to the specific agricultural and technological conditions of the country. India has great agricultural potential, with a variety of crops growing in different agro-climatic conditions. At the same time, the country is experiencing a rapid technological upswing and has a young, tech-savvy population.

In addition, India faces the problem of land fragmentation, as many farmers have smaller land parcels. NaLamKI could enable these farmers to use their limited resources more efficiently by providing accurate data and recommendations tailored to their specific conditions.

However, cultural and infrastructural factors also need to be considered when implementing NaLamKI in India. It is important to design the technology in such a way that it is accessible to people with different levels of education and technical knowledge. Similarly, it is crucial to involve local communities in the process to ensure that the solutions meet their real needs.

Thank you, Dr. Sebastian Bosse, for your time!