AI-Driven Livestock Surveillance Platform

AI-driven platform for real-time livestock health monitoring, enabling early disease detection, biosecurity tracking, and outbreak prevention through risk analytics and smart dashboards.

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Inspiration

The inspiration for this project came from a real incident at my uncle’s poultry farm, where a sudden disease outbreak led to significant financial losses due to late detection, along with uncertainty about whether infected eggs had already been sold and could harm consumers. While this was a small-scale farm, it exposed a much larger problem—on a national level, outbreaks like Avian Influenza (Bird Flu) and Swine Flu can lead to the loss of lakhs of livestock and pose serious risks to human health. This motivated us to build a system that enables early detection, monitoring, and prevention of such outbreaks.

What it does

The AI-Driven Livestock Surveillance Platform is a digital biosecurity system designed for poultry and pig farms that allows farmers to log daily biosecurity checklists, upload livestock images for AI-based symptom analysis, receive automated risk scores (Safe, Moderate, High), and get early alerts with preventive recommendations, while also enabling veterinarians and authorities to monitor farms through real-time dashboards, identify high-risk farms using geospatial maps, and track incidents for effective outbreak management; the system computes risk using a combination of compliance and AI inference defined as ( RiskScore = \alpha \cdot ChecklistScore + \beta \cdot AIConfidence + \gamma \cdot IncidentFactor ).

How we built it

We developed the platform using a full-stack architecture where the frontend was built with React.js, TypeScript, and Tailwind CSS for responsive UI, Recharts for analytics visualization, and Leaflet for interactive geospatial farm mapping, while integrating the Google Gemini API for image-based symptom detection and recommendations, and designing a scalable backend structure using PostgreSQL to manage farms, incidents, alerts, and risk scoring, combining modules such as checklist logging, AI analysis, and real-time monitoring dashboards.

Challenges we ran into

We faced multiple challenges including designing a solution that is usable by farmers with low digital literacy, ensuring consistent and structured outputs from the Gemini API, building a reliable risk scoring system combining checklist data and AI predictions, handling performance and rendering issues in map-based visualization with clustering of multiple farms, and creating a scalable architecture without access to real-world datasets.

Accomplishments that we're proud of

We successfully built an end-to-end prototype for farm biosecurity monitoring, integrated AI-based symptom detection into a practical workflow, designed multi-role dashboards for farmers, veterinarians, and government authorities, developed an intuitive UI for real-time alerts and decision-making, and created a scalable system architecture ready for real-world deployment.

What we learned

Through this project, we learned the importance of early detection in preventing livestock disease outbreaks, how to integrate AI into real-time monitoring systems, how to design scalable backend architectures for multi-entity platforms, how to build geospatial dashboards for large-scale monitoring, and how to structure effective prompts for reliable AI responses.

What's next for AI-Driven Livestock Surveillance Platform

In the future, we plan to train dedicated machine learning models using real poultry and pig disease datasets, enable offline-first mobile support for rural farmers, integrate SMS and WhatsApp alerts for real-time communication, add IoT sensor integration such as temperature and humidity monitoring, implement predictive outbreak forecasting using time-series models, and expand the platform to support more livestock categories, with the goal of scaling it into a nationwide system for proactive livestock health monitoring and outbreak prevention.

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