Sensors for Digital Transformation in Smart Forestry.

Area of Science:

• Forestry Science
• Artificial Intelligence
• Robotics
• Sensor Technology

Background:

• Smart forestry, driven by artificial intelligence (AI), promises enhanced forest management and reduced environmental impact.
• Effective AI implementation in forestry relies heavily on the availability of extensive, high-quality data.
• Challenging forest environments pose significant obstacles to traditional data collection methods.

Purpose of the Study:

• To highlight the critical role of sensor-based data acquisition in the digital transformation of forestry.
• To emphasize the integration of sensor technologies for standardized, high-quality data generation essential for AI.
• To explore the synergy between human expertise and digital transformation through a human-in-the-loop approach.

Main Methods:

• Deployment of autonomous robotic systems for data collection and processing within forest environments.
• Integration of a universal sensor platform to facilitate sensor deployment and data generation.
• Implementation of a human-in-the-loop approach for expert-guided data generation and adaptability.

Main Results:

• Autonomous robotic systems effectively function as mobile data collectors and processing hubs in forests.
• The universal sensor platform aids in sensor integration and the generation of substantial volumes of quality data.
• The initial phase of data generation is critical for successful digital transformation in forestry.

Conclusions:

• Comprehensive, high-quality data generation is the cornerstone of advancing smart forestry.
• The careful selection of appropriate sensors is paramount for the success of AI applications in forestry.
• Integrating human expertise with autonomous systems enhances the adaptability and effectiveness of smart forestry initiatives.