Sensors play a crucial role in the digital transformation of smart forestry, enabling the collection of high-quality data essential for artificial intelligence (AI) applications. Smart forestry integrates advanced technologies like AI and sensors into forest management and conservation, aiming to enhance sustainable practices, biodiversity protection, and climate change mitigation. The effectiveness of AI in this domain depends on the availability of extensive, high-quality data, which is facilitated by sensor-based data acquisition. However, the complexity and challenging conditions of forest environments often hinder data collection efforts. Achieving the full potential of smart forestry requires a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data. This paper highlights the symbiotic relationship between human expertise and digital transformation in forestry, particularly under challenging conditions. A human-in-the-loop approach allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. Autonomous robotic systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. The paper presents a universal sensor platform, detailing experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and findings underscore its significance in advancing smart forestry. The article discusses various sensor technologies, including GNSS for location, temperature and humidity sensors, soil pH sensors, and imaging technologies like RGB, thermal, and LiDAR. These sensors are essential for monitoring forest conditions, detecting fires, assessing tree health, and monitoring biodiversity. The integration of these technologies with AI and robotics enables efficient and effective forest management. The paper also explores the challenges and solutions in deploying and operating these sensors in forest environments, emphasizing the need for reliable communication and data collection methods. The integration of human expertise with sensor technologies and AI is crucial for successful digital transformation in smart forestry.Sensors play a crucial role in the digital transformation of smart forestry, enabling the collection of high-quality data essential for artificial intelligence (AI) applications. Smart forestry integrates advanced technologies like AI and sensors into forest management and conservation, aiming to enhance sustainable practices, biodiversity protection, and climate change mitigation. The effectiveness of AI in this domain depends on the availability of extensive, high-quality data, which is facilitated by sensor-based data acquisition. However, the complexity and challenging conditions of forest environments often hinder data collection efforts. Achieving the full potential of smart forestry requires a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data. This paper highlights the symbiotic relationship between human expertise and digital transformation in forestry, particularly under challenging conditions. A human-in-the-loop approach allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. Autonomous robotic systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. The paper presents a universal sensor platform, detailing experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and findings underscore its significance in advancing smart forestry. The article discusses various sensor technologies, including GNSS for location, temperature and humidity sensors, soil pH sensors, and imaging technologies like RGB, thermal, and LiDAR. These sensors are essential for monitoring forest conditions, detecting fires, assessing tree health, and monitoring biodiversity. The integration of these technologies with AI and robotics enables efficient and effective forest management. The paper also explores the challenges and solutions in deploying and operating these sensors in forest environments, emphasizing the need for reliable communication and data collection methods. The integration of human expertise with sensor technologies and AI is crucial for successful digital transformation in smart forestry.