AI for Sustainability: Real-Time Computer Vision for Environmental Monitoring and Conservation

The increasing threats of climate change, deforestation, and biodiversity loss demand innovative technological solutions for real-time environmental monitoring and conservation. The contributions and contributions of this work include an AI-Driven Adaptive Environmental Surveillance System (AESS), with computer vision, edge AI, federated learning, and neuromorphic computing, to autonomously, efficiently, and privacy-protecting environmental surveillance. The system additionally combines multi-modal detection of RGB imaging, infrared, LiDAR and hyperspectral data, raising the detection accuracy. Along with providing a bio-inspired swarm AI drones and ground-based robotic units for real-time ecological parameter assessment of real-time illegalities, their eyes and ears are the swarm AI drones and ground-based robotic units. The adaptive AI models, armed with reinforcement learning, undergo continuous improvement in terms of the detection accuracy owing to the variations in the environment. The framework of the secure, transparent, immutable data storage for conservation data is based on the blockchain technology. The experimental results reveal that automation achieved 95% reduction of manual surveillance, 80% decrease in the response time to illegal activities, 50% energy saving as compared to traditional AI models. This work furthers sustainability of environmental monitoring by producing a more efficient, scalable, and applicable means of using AI. AESS, proposed as a transformative step to autonomous, real-time, environmental protection in the face of critical conservation challenges, is evidence of secure data, energy efficient, with minimal human intervention as possible. Future work extends the real world deployment scenarios, as well as makes AI more adaptable.

Ҳали таржима қилинмаган