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AI, IoT and modeling technologies for agriculture, environmental monitoring and resource management

Published: November 2025

AI, IoT, and modeling technologies for agriculture, environmental monitoring, and resource management rely on deep learning, IoT, and machine learning to precisely analyze environmental conditions, make agricultural processes more efficient, and predict natural hazards early. These technologies offer innovative opportunities to optimize resource management in agriculture, water and energy supply, and nature conservation. They not only enable adaptation to specific geographical and climatic conditions but also increase efficiency and sustainability in various sectors. For example, the precise collection and processing of data allows farmers to optimize the targeted use of water, fertilizers, and pesticides. Similarly, IoT-based sensors and AI models help monitor and adjust water and energy consumption in diverse environmental and industrial scenarios.

Guiding questions

  • How can predictive AI models be used to better predict and manage natural disasters and extreme weather events?
  • What might future infrastructures for early warning systems and crisis management look like? In which areas can the use of water, energy, and pesticides be optimized through precision agricultural technologies?
  • What are the long-term ecological impacts of using digital technologies in natural areas?
  • To what extent can AI and IoT be tailored to local conditions, and what does this mean for country-specific agricultural and environmental strategies?
Image description: Drone over a field, collecting agricultural data and monitoring plants.

challenges

A major challenge is the diversity and quality of the data: many sources – such as drones, ground sensors, and satellite imagery – must be integrated and harmonized, requiring high technical standards and efficient infrastructure. Data protection and security risks also arise. Furthermore, the costs of implementing these technologies are considerable and can overwhelm smaller businesses, especially since many rural areas often lack the necessary digital infrastructure, such as broadband internet. Effective use of these technologies requires a high level of expertise, necessitating comprehensive training for users.

Concrete examples

  • Precision agriculture (automated drone monitoring, IoT soil sensors)
  • Environmental monitoring (satellite-based biodiversity monitoring, IoT-supported wildfire sensors)
  • Resource efficiency and water management (smart irrigation systems, modeling of water distribution systems)
  • Early warning systems (floods) and drought monitoring
  • Automated harvesting machines (reduction of food losses)
  • Blockchain and IoT for traceability
Image description: Foresight – Thuringia, Blue Eye in focus

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Reference

Your contact persons

Reference

Michel Reichardt

Michel Reichardt Project Manager Strategic Foresight

Christoph Grollman

Christoph Grollman Project Manager Strategic Foresight

Dr. Sophia Gänßle

Dr. Sophia Gänßle Project Manager Data Science Strategic Foresight

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