Multimodal Artificial Intelligence Framework for Real-Time Wildlife and Habitat Monitoring

Abstract

Introduction: Populations of animals adapt rapidly to habitat fragmentation, climatic variability, water deficiency, vegetation changes, and human disturbances. Ongoing monitoring is hence needed, yet traditional field surveys are sporadic and might fail to detect nocturnal or seasonally active species. Materials and Methods: This paper proposes a multimodal AI-based model incorporating camera trap images, audio recordings, microclimate data, and habitat characteristics. Observation data is geolocated and timestamped, filtered based on poor-quality records, and analyzed to estimate animal presence and activity as well as the stress level of the environment. The pilot validation employed 12,600 observation records of animals and their habitat conditions for mammals, birds, amphibians, and control cases at forest edges, grasslands, and wetlands. Results and Discussion: The proposed multimodal framework provided a balanced accuracy of 93.6% and an F1 score of 92.8% for wildlife event recognition outperforming single-modality solutions.  Temperature, presence of water, openness of the forest canopy, and presence of human noise turned out to be the strongest context indicators. Multi-modal approach helped to detect animals even during low-light conditions and distinguish real absence of animals from faint sensor signals. Conclusion: The suggested approach makes it possible to conduct quick and non-invasive research with ecological interpretation being its main purpose. It will allow conservationists to spot the changes in the behavior of the animals or destruction of their habitat earlier and thus prioritize field surveys