How Dalys leverages multisensor AI technologies in Remote Areas

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In remote environments like forests, implementing multisensor AI technologies presents both challenges and exciting opportunities. Advanced solutions such as Computer Vision, Sound Recognition, and IoT Sensors enable real-time monitoring and analysis in even the most inaccessible locations. Do you know how these technologies can be used in remote areas, and why they can make a difference?

1. Monitor biodiversity

Forests are home to a significant portion of our planet’s biodiversity. With advanced computer vision and sound recognition models, we can deploy wildlife cameras and sound sensors to:

  • Identify and track wildlife: Smart AI algorithms, applied on image or sound data, automatically detect and monitor animal activity, providing valuable insights about the local biodiversity.
  • Monitor endangered species: Automated alerts notify responsible parties—such as conservationists, researchers, or policymakers—when rare species are detected.

2. Monitor soil conditions

Monitoring soil health in remote areas can be challenging due to limited access. However, by integrating IoT sensors, which provide real-time data collection, it is possible to overcome these barriers even in the most inaccessible locations.

  • Smart soil monitoring: providing real-time data helps detect changes or imbalances in soil health like soil moisture, temperature, pH and nutrient levels.
  • Advanced analytics: Machine learning algorithms can predict future conditions by identifying patterns, trends, and anomalies in soil health over time. This helps responsible parties anticipate issues such as drought stress, nutrient deficiencies, or soil degradation. By analyzing historical and real-time data, these insights enable the development of strategies to enhance nature conservation.

3. Wildfire management

Wildfires pose a significant risk in many remote areas. Computer vision can play a crucial role here:

  • Early detection: cameras can identify smoke or fire hotspots in real-time, allowing for immediate action before the fire has a chance to spread, potentially preventing large-scale damage.

Practical implementation

To maximize the effect of applying multisensor AI solutions in remote areas, we can combine the technologies:

  • Edge computing: Using mini computers like Raspberry Pi or Jetson modules, we can process data locally without relying on an internet connection.
  • IoT sensors: Integrated networks of sensors (camera, sound, soil…) provide continuous monitoring and updates.
  • Drones: Provide the capability to efficiently scan large areas.

Challenges and solutions

While the possibilities are vast, there are some challenges:

  1. Limited connectivity: Internet is often unavailable in forests. Local storage and periodic data uploads can address this issue.
  2. Power supply: Solar panels and other off-grid solutions can power devices.
  3. Model accuracy: Training AI models on diverse datasets improves accuracy, especially for specific fauna and flora.

Why this matters

At Dalys, we aim to use technology for a better world. By applying these multisensor AI solutions in remote areas, we can:

  • Continuously monitor environmental health with advanced analytics.
  • Enhance nature conservation.
  • Preserve endangered species and ecosystems.

Let’s harness the power of innovation to support even the most precious and remote natural areas. Interested in collaborating or learning more about our approach? Contact us!

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