Sensors and Networks in Agriculture


The new innovation network “SuNinLand – Sensors and Networks in Agriculture” will contribute to the digitalization of agriculture with innovative and user-driven technology developments starting in spring 2023.

Along the following themes, the network will be developed:

Agricultural machinery: Platform-independent sensor technology.
Examples: Sensors for determining the position of vehicles, sensor technology for monitoring the condition of agricultural machinery (acceleration sensors, angular rate sensors, etc.), digital solutions for crop monitoring and processing.

Plants: Single plant detection and non-destructive plant phenotyping.
Examples: Nutritional status of plants, disease and pest infestation and quality of harvest-ready products (biomass, chlorophyll content), ingredients of grain crops and legumes (protein, oil), dry matter determination, crude protein feed.

Soil: Soil Condition Survey.
Examples: Sensors for soil moisture, nutrient balance, conductivity, pH, pest infestation, organic matter, etc.

Data: Sensor networks for decision support systems and data management. Examples: Data volume reduction, pre-processing on the sensor, real-time data acquisition and analysis (tactile internet), data evaluation algorithms using artificial intelligence.
Your Topics: Which developments are being driven in your company?

Strategic focus of the network

The network focuses on the development of concepts, processes, technologies and applications for the digitalization of agriculture. Novel, sensor data-based technology modules are to be developed that make a measurable contribution to sustainable agriculture.

Objectives and specific development needs

The objective of the network is to research and develop methods, procedures, techniques and exemplary applications to collect data on the condition of plants, soil and machines in the context of digital agriculture using heterogeneous sensor systems, to process these data using suitable data processing procedures and to make them available to users.

In the network, various technical and technological sub-goals are to be developed and tested on the basis of concrete use cases. The R&D activities required for this address the following specific development needs, among others:

  • Modular structure: Enabling cross-application technology and process modules through modular design and standards-based interfaces to common farm management systems and agricultural equipment and machinery.
  • High degree of automation: Enabling large-scale applications through automated data processing as well as automated and self-learning analysis of the collected data (using machine learning and deep learning).
  • Data integrity: Ensuring the integrity and security of the data collected, among other things, to create broad acceptance among farmers and overcome existing skepticism and prejudice.
  • Traceability: Support of technical experts in the evaluation of the collected data and for the derivation of recommendations for action for farmers.
  • Cost efficiency: Against a backdrop of skyrocketing costs and purchasing prices at ever new all-time highs, more and more analysts and economists are expecting significantly shrinking profits for farmers in 2023. The solutions to be developed in the network can therefore only be successful in the market if they are cost-efficient.
  • Long-term stability and robustness: Development of maintenance-free and autonomously operating systems that simultaneously guarantee high reliability in the prevailing extreme environmental conditions
  • Adaptation for smaller markets: The technology and process modules to be developed should be designed in such a way that they also enable the partners to access marginal markets (blue green infrastructure: strategic spatial planning for near-natural areas with different natural features in cities).

Your contact persons

Ralf Ryter

+49 331 2734 494 3
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Matthias Richter

+49 331 2734 494 4
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Initial project approaches from the network focus on:

  • Densification and cost reduction of sensor networks in the field.
  • Use of AI in plant condition detection from optical sensors on microcomputers.

Are you interested in the SuNinLand network or do you already have project ideas?

Do you have questions about funding or about the services of GEOkomm?

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