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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 leverage deep learning, the IoT, and machine learning to precisely analyze environmental conditions, make agricultural processes more efficient, and predict natural hazards at an early stage. These technologies offer innovative ways 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. Through the precise collection and processing of data, farmers can, for example, specifically optimize the use of water, fertilizers, and pesticides. Similarly, IoT-based sensors and AI models help monitor and adjust water and energy consumption in different environmental and industrial scenarios.

Guiding questions

  • How can natural disasters and extreme weather events be better predicted and managed using predictive AI models?
  • What could 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 agriculture 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?
Bildbeschreibung / 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 images – must be integrated and harmonized, which requires high technical standards and powerful infrastructure. In addition, there are data protection and security risks. The costs of using the technologies are also considerable and can overwhelm smaller businesses in particular, especially since many rural regions often lack the necessary digital infrastructure such as broadband internet. It requires a high level of expertise to use the technologies effectively, which necessitates comprehensive training measures for the users.

Concrete examples

  • Precision agriculture (automated drone monitoring, IoT soil sensors)
  • Environmental monitoring (satellite-controlled 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
Bildbeschreibung / description: Foresight – Thuringia, Blue Eye in Focus

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Reference

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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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