AI-Driven Adaptive Agro-Wildlife Overlap Prediction and Mitigation
– Bipendra Basnyat
- Research Objectives –
The primary objective of this research is to develop an advanced AI-driven system capable of predicting and mitigating the long-term impacts of agricultural development and infrastructure construction on wildlife habitats and movement patterns. This system will integrate multi-modal data sources, including satellite imagery, ecological surveys, climate data, and historical wildlife movement patterns, to create dynamic, high-resolution models of agro-wildlife interactions. The AI will be designed to adapt to changing environmental conditions and human activities, providing continuous, updated predictions of how infrastructure projects such as road construction, fencing, and agricultural expansion affect wildlife over extended periods, potentially spanning decades.
A key focus of the research will be on developing AI algorithms that can simulate and predict complex, cascading effects of human interventions on ecosystems. This includes modeling changes in migration routes, breeding patterns, and population dynamics of various species in response to landscape alterations. The system will also incorporate socio-economic factors to balance human development needs with wildlife conservation, proposing optimal strategies for land use that minimize negative impacts on biodiversity while supporting sustainable agricultural growth. By providing data-driven insights and actionable recommendations, this research aims to inform policy-making, guide sustainable development practices, and foster harmonious coexistence between agricultural communities and wildlife in shared landscapes.
- Research Contributions –
- The research will produce a predictive AI model for agro-wildlife interactions, developed using machine learning on historical data and satellite imagery.
- A long-term impact assessment tool for infrastructure projects will be created through the integration of ecological models with AI prediction algorithms.
- An optimal land-use strategy generator will be designed using multi-objective optimization techniques and scenario simulations.
- The project will build a wildlife migration pattern simulator with agent-based modeling, validated against GPS tracking data.
- Finally, a policy recommendation framework for sustainable development will be formulated by combining AI predictions with expert knowledge and stakeholder input.
Terrain Analysis Using MMwave Radar To Detect Objects Beneath The Surfaces like Soil, Grass (long-short), Snow
- Research Objectives –
- This research focuses on using mmWave radar for terrain analysis to detect objects hidden beneath surfaces like soil, grass, and snow. It is crucial for advancing autonomous systems, especially in off-road environments where accurate perception is challenging due to terrain variability. The key challenges lie in differentiating between surface types and accurately detecting objects buried under different conditions, such as varying grass lengths or snow cover. The future vision is to create reliable sensing systems that improve the safety and efficiency of autonomous exploration in complex and unstructured terrains.
- Research Contributions –
Our research contributes to enhancing the perception capabilities of autonomous systems, particularly in off-road environments. By leveraging mmWave radar for subsurface object detection, we aim to push the boundaries of current terrain analysis techniques. This will significantly benefit fields like robotics, agriculture, and environmental monitoring, where understanding what’s beneath the surface is critical. Ultimately, this work lays the foundation for more intelligent and resilient autonomous systems capable of navigating challenging terrains.