Title : Pahadi.AI: An AI-enabled environmental intelligence system for conserving Himalayan wild bioresources and ecosystem health
Abstract:
The Himalayan region supports a diverse range of wild bioresources that contribute not only to local food systems and livelihoods but also to ecosystem functioning, biodiversity and soil health. However, knowledge about these species, their ecological roles and traditional conservation practices remains fragmented across scientific datasets and community-held oral knowledge. Climate change, land-use change and increasing commercialization are further altering the distribution, phenology, availability and harvesting pressure of these resources. This study proposes Pahadi.AI as an AI-enabled environmental intelligence platform for integrating local ecological knowledge with scientific and geospatial data to support conservation of Himalayan wild bioresources.
The proposed system will combine community-based knowledge collection, artificial intelligence, natural-language processing, GIS, remote sensing, climate datasets and ecological observations. Local knowledge holders will be engaged to document species occurrence, traditional uses, harvesting practices, phenology, ecological functions and locally observed environmental changes. Selected species such as Myrica esculenta (Kaphal), Berberis asiatica and Hippophae salicifolia will serve as initial case studies. AI-assisted processing will structure this information into a searchable species-level knowledge repository and link it with spatial, climatic and ecological datasets.
The platform is envisioned to move beyond documentation towards predictive conservation intelligence by identifying areas and species potentially vulnerable to climate and anthropogenic pressures, detecting changes in resource availability and ecological conditions, and identifying locally recognized indicators of ecosystem and soil health. In the longer term, predictive models could assist communities, researchers and forest managers in prioritizing species and landscapes for monitoring, sustainable harvesting, restoration and conservation interventions.
The study demonstrates how AI can function as a bridge between traditional ecological knowledge, environmental data and conservation decision-making, creating a community-linked digital infrastructure for protecting Himalayan wild bioresources and the ecosystem services they support.
Keywords: Artificial intelligence; environmental intelligence; Himalayan biodiversity; wild bioresources; traditional ecological knowledge; predictive analytics; ecosystem health; soil health; conservation.
