prof-freakenstein/SIH2025 huggingface dataset:
Blockchain-Based Blue Carbon Registry and MRV System
Organization: Ministry of Earth Sciences (MoES)
Category: Software | Theme: Clean & Green Technology

Background

Blue carbon ecosystem restoration is gaining importance in India’s climate strategy. However, there is no decentralized, verifiable Monitoring, Reporting, and Verification (MRV) system that ensures transparency, accuracy, and carbon credit generation.

Description

Participants are to design a Blockchain-powered registry where:
• Verified plantation and restoration data are immutably stored.
• Carbon credits are tokenized using smart contracts.
• NGOs, communities, and coastal panchayats can be onboarded.
• Field data is integrated from apps and drones.

Expected Solution

• Blockchain app for blue carbon MRV.
• Smart contracts for tokenized credits.
• Mobile interface for data uploads.
• Admin tools for NCCR.
Integrated Platform for Crowdsourced Ocean Hazard Reporting and Social Media Analytics
Organization: Ministry of Earth Sciences (MoES)
Category: Software | Theme: Disaster Management

About INCOIS

The Indian National Centre for Ocean Information Services (INCOIS), operating under the administrative control of the Ministry of Earth Sciences, provides ocean information and advisory services to support disaster risk reduction and ensure maritime safety for coastal stakeholders. Its early warning services cover hazards such as tsunamis, storm surges, high waves, swell surges, and coastal currents, enabling authorities and communities to make informed decisions during ocean-related emergencies.

Background

India’s vast coastline is vulnerable to a range of ocean hazards such as tsunamis, storm surges, high waves, coastal currents, and abnormal sea behaviour. While agencies like INCOIS provide early warnings based on satellite data, sensors, and numerical models; real-time field reporting from citizens and local communities are often unavailable or delayed. Additionally, valuable insights from public discussions on social media during hazard events remain untapped, yet can be critical for understanding ground realities, public awareness, and the spread of information.

Detailed Description

There is a need for a unified platform that enables citizens, coastal residents, volunteers, and disaster managers to report observations during hazardous ocean events (e.g., unusual tides, flooding, coastal damage, tsunami, swell surges, high waves, etc.) and to monitor public communication trends via social media.

This platform should:
• Allow citizens to submit geotagged reports, photos, or videos of observed ocean hazards via a mobile/web app.
• Support role-based access for citizens, officials, and analysts.
• Aggregate and visualize real-time crowdsourced data on a dynamic dashboard.
• Visualize all crowdsourced reports and social media indicators on an interactive map, with hotspots dynamically generated based on report density, keyword frequency, or verified incidents.
• Integrate social media feeds (e.g., Twitter, public Facebook posts, YouTube comments) and apply Text Classification/Natural Language Processing to extract hazard-related discussions and trends.
• Help emergency response agencies understand the scale, urgency, and sentiment of hazard events.
• Provide filters by location, event type, date, and source, enabling better situational awareness and faster validation of warning models.

Expected Solution

An integrated software platform (mobile + web) with:
• User registration and reporting interface with media upload.
• Map-based dashboard showing live crowd reports and social media activity.
• Dynamic hotspot generation based on report volume or verified threat indicators.
• Backend database and API for data management and integration with early warning systems.
• NLP engine for detecting relevant hazard-related posts, keywords, and engagement metrics.
• Multilingual support for regional accessibility.
• Offline data collection capabilities (sync later), useful for remote coastal areas.
FloatChat - AI-Powered Conversational Interface for ARGO Ocean Data Discovery and Visualization
Organization: Ministry of Earth Sciences (MoES)
Category: Software | Theme: Miscellaneous

 
Background

Oceanographic data is vast, complex, and heterogeneous – ranging from satellite observations to in-situ measurements like CTD casts, Argo floats, and BGC sensors. The Argo program, which deploys autonomous profiling floats across the world’s oceans, generates an extensive dataset in NetCDF format containing temperature, salinity, and other essential ocean variables. Accessing, querying, and visualizing this data requires domain knowledge, technical skills, and familiarity with complex formats and tools. With the rise of AI and Large Language Models (LLMs), especially when combined with modern structured databases and interactive dashboards, it is now feasible to create intuitive, accessible systems that democratize access to ocean data.

Description

The current problem statement proposes the development of an AI-powered conversational system for ARGO float data that enables users to query, explore, and visualize oceanographic information using natural language.

The current system shall:
− Ingest ARGO NetCDF files and convert them into structured formats (like SQL/Parquet).
− Use a vector database (like FAISS/Chroma) to store metadata and summaries for retrieval.
− Leverage Retrieval-Augmented Generation (RAG) pipelines powered by multimodal LLMs (such as GPT, QWEN, LLaMA, or Mistral) to interpret user queries and map them to database queries (SQL). (Use Model Context Protocol (MCP))
− Enable interactive dashboards (via Streamlit or Dash) for visualization of ARGO profiles, such as mapped trajectories, depth-time plots, and profile comparisons, etc.
− Provide a chatbot-style interface where users can ask questions like:
  • Show me salinity profiles near the equator in March 2023
  • Compare BGC parameters in the Arabian Sea for the last 6 months
  • What are the nearest ARGO floats to this location?

This tool will bridge the gap between domain experts, decision-makers, and raw data by allowing non-technical users to extract meaningful insights effortlessly.

Expected Solution

− End-to-end pipeline to process ARGO NetCDF data and store it in a relational (PostgreSQL) and vector database (FAISS/Chroma).
− Backend LLM system that translates natural language into database queries and generates responses using RAG.
− Frontend dashboard with geospatial visualizations (using Plotly, Leaflet, or Cesium) and tabular summaries to ASCII, NetCDF.
− Chat interface that understands user intent and guides them through data discovery.
− Demonstrate a working Proof-of-Concept (PoC) with Indian Ocean ARGO data and future extensibility to in-situ observations (BGC, glider, buoys, etc.), and satellite datasets.

Acronyms

NetCDF: Network Common Data Format
CTD: Conductivity Temperature and Depth
BGC: Bio-Geo-Chemical Floats
Identifying Taxonomy and Assessing Biodiversity from eDNA Datasets
Organization: Ministry of Earth Sciences (MoES)
Category: Software | Theme: Miscellaneous

Background

The deep ocean, encompassing vast and remote ecosystems like abyssal plains, hydrothermal vents, and seamounts, harbors a significant portion of global biodiversity, much of which remains undiscovered due to its inaccessibility. Understanding deep-sea biodiversity is critical for elucidating ecological interactions (e.g., food webs, nutrient cycling), informing conservation strategies for vulnerable marine habitats, and identifying novel eukaryotic species with potential biotechnological or ecological significance.

Environmental DNA (eDNA) has emerged as a powerful, non-invasive tool for studying these ecosystems by capturing genetic traces of organisms from environmental samples, such as seawater or sediment, without the need for physical collection or disturbance of fragile habitats. By targeting marker genes like 18S rRNA or COI, eDNA enables the detection of diverse eukaryotic taxa, including protists, cnidarians, and rare metazoans, offering insights into species richness and community structure.

Description

The Centre for Marine Living Resources and Ecology (CMLRE) will undertake routine voyages to the deep sea and collect sediment and water samples from hotspot regions for biodiversity assessment and ecosystem monitoring. The water and sediment samples will be used to extract eDNA and will be subject to high-throughput sequencing.

However, assigning raw eDNA sequencing reads to eukaryotic taxa or inferring their ecological roles presents significant challenges, primarily due to the poor representation of deep-sea organisms in reference databases like SILVA, PR2, or NCBI. These databases, built primarily from well-studied terrestrial or shallow-water species, lack comprehensive sequences for deep-sea eukaryotes, leading to misclassifications, unassigned reads, or underestimation of biodiversity.

Traditional bioinformatic pipelines for eDNA analysis, such as those implemented in QIIME2, DADA2, or mothur, rely heavily on sequence alignment or mapping to these databases, which is inadequate for novel or divergent deep-sea taxa. This dependency limits the discovery of new species and hinders accurate biodiversity assessments, critical for conservation in rapidly changing deep-sea environments. The computational time required for processing eDNA data exacerbates these challenges, particularly given the limitations of database-dependent methods and the complexity of eDNA datasets.

Expected Solution

To address the challenges of poor database representation and computational time in deep-sea eDNA analysis, we propose an AI-driven pipeline that uses deep learning and unsupervised learning to identify eukaryotic taxa and assess biodiversity directly from raw eDNA reads. The solution should be able to classify the sequences, annotate and estimate abundance. This solution minimizes reliance on reference databases, reduces computational time through optimized workflows, and enables the discovery of novel taxa and ecological insights in deep-sea ecosystems.
Embedded Intelligent Microscopy System for Identification and Counting of Microscopic Marine Organisms
Organization: Ministry of Earth Sciences (MoES)
Category: Hardware | Theme: Smart Automation

Background

Marine biodiversity assessments rely significantly on microscopic analysis of planktonic organisms, including phytoplankton, zooplankton, and other microscopic marine life. Traditionally, counting and species identification require manual microscopic examination, which is labor-intensive, subjective, and prone to human error. AI-enabled embedded microscopy systems provide an effective alternative by automating the identification, classification, and enumeration of these organisms directly at the point of sampling.

Description

This problem statement envisages developing an embedded intelligent system that can be integrated directly with microscopic imaging devices used in marine biological studies. The system should leverage embedding-based deep learning techniques to identify and count microscopic marine organisms captured through microscopy images. The embedded platform should be optimized for low computational resources, enabling accurate, rapid, and automated analysis without manual intervention.

The solution should specifically handle diverse challenges encountered in microscopic imaging, such as overlapping organisms, variations in morphology, illumination inconsistencies, and imaging artifacts. It must accurately classify and quantify different species present in microscopic images to facilitate marine biodiversity assessments efficiently.

Expected Solution

An embedded microscopy-integrated AI system featuring:
• Lightweight deep learning models deployable on embedded computational hardware.
• Automatic species/genus-level identification of microscopic marine organisms from microscopy images.
• Accurate and automated counting of individual organisms per species.
• Capability for onboard processing, storage, and reporting of species identification and counts for subsequent ecological analysis.
AI-Powered Crop Yield Prediction and Optimization
Organization: Government of Odisha
Category: Software | Theme: Agriculture FoodTech & Rural Development

Description

Develop an AI-based platform to predict crop yields using historical agricultural data, weather patterns, and soil health metrics. The system should provide actionable recommendations for farmers to optimize irrigation, fertilization, and pest control, tailored to specific crops and regional conditions.

Expected Outcome

A scalable software solution (web/mobile app) that helps small-scale farmers increase productivity by at least 10% through data-driven insights, with an interface supporting regional languages for accessibility.

Technical Feasibility

Utilizes machine learning models (e.g., regression, neural networks) trained on open-source agricultural datasets, integrated with APIs for real-time weather and soil data.
Blockchain-Based Supply Chain Transparency for Agricultural Produce
Organization: Government of Odisha
Category: Software | Theme: Agriculture FoodTech & Rural Development

Description

Create a blockchain-based system to track agricultural produce from farm to consumer, ensuring transparency in pricing, quality, and origin. The solution should allow stakeholders (farmers, distributors, retailers) to verify transactions and reduce exploitation in the supply chain.

Expected Outcome

A decentralized platform with a user-friendly interface for farmers and consumers to trace produce, reducing fraud and ensuring fair pricing, deployable on low-cost hardware or cloud infrastructure.

Technical Feasibility

Leverages existing blockchain frameworks like Ethereum or Hyperledger, with smart contracts for automated tracking and QR code integration for consumer access.
Smart Waste Segregation and Recycling System
Organization: Government of Odisha
Category: Hardware | Theme: Clean & Green Technology

Description

Design an IoT-enabled waste segregation system using sensors and machine learning to automatically classify household waste (organic, recyclable, hazardous) at the source. The system should integrate with municipal waste management for efficient collection and recycling.

Expected Outcome

A prototype device with 90% accuracy in waste classification, coupled with a mobile app for households to monitor waste disposal and earn incentives for recycling.

Technical Feasibility

Uses affordable sensors (e.g., cameras, weight sensors) and ML models (e.g., convolutional neural networks) for waste identification, deployable in urban and semi-urban areas.
Disaster Response Drone for Remote Areas
Organization: Government of Odisha
Category: Hardware | Theme: Robotics and Drones

Description

Develop a drone-based system to deliver medical supplies and communication devices to remote areas during natural disasters (floods, earthquakes). The drone should use AI for real-time navigation and obstacle avoidance, with a payload capacity of at least 5 kg.

Expected Outcome

A working drone prototype with a mobile app for disaster management teams to coordinate deliveries, reducing response time by 20% in inaccessible regions.

Technical Feasibility

Employs existing drone hardware with open-source flight control software (e.g., Ardupilot) and AI algorithms for navigation, tested in simulated disaster scenarios.
Gamified Learning Platform for Rural Education
Organization: Government of Odisha
Category: Software | Theme: Smart Education

Description

Build a gamified digital platform to enhance learning outcomes for students in rural schools (grades 6-12), focusing on STEM subjects. The platform should use interactive games, multilingual content, and offline access to engage students with limited internet connectivity.

Expected Outcome

A mobile app or web platform increasing student engagement by 15%, with analytics for teachers to track progress, deployable on low-cost devices.

Technical Feasibility

Uses HTML5/CSS for offline-compatible web apps, integrated with open-source gamification frameworks and localized content delivery.
AI-Driven Public Health Chatbot for Disease Awareness
Organization: Government of Odisha
Category: Software | Theme: MedTech / BioTech / HealthTech

Description

Create a multilingual AI chatbot to educate rural and semi-urban populations about preventive healthcare, disease symptoms, and vaccination schedules. The chatbot should integrate with government health databases and provide real-time alerts for outbreaks.

Expected Outcome

A chatbot accessible via WhatsApp or SMS, reaching 80% accuracy in answering health queries and increasing awareness by 20% in target communities.

Technical Feasibility

Built using NLP frameworks (e.g., Rasa, Dialogflow) with APIs for health data integration, deployable on cloud platforms for scalability.
Smart Traffic Management System for Urban Congestion
Organization: Government of Odisha
Category: Software | Theme: Transportation & Logistics

Description

Design an AI-based traffic management system to optimize signal timings and reduce congestion in urban areas. The system should analyze real-time traffic data from cameras and IoT sensors to predict and mitigate bottlenecks.

Expected Outcome

A software prototype reducing average commute time by 10% in a simulated urban environment, with a dashboard for traffic authorities to monitor and control signals.

Technical Feasibility

Uses computer vision (e.g., OpenCV) and reinforcement learning for traffic prediction, integrated with existing traffic camera networks.
Renewable Energy Monitoring System for Microgrids
Organization: Government of Odisha
Category: Hardware | Theme: Renewable / Sustainable Energy

Description

Develop an IoT-based monitoring system for solar or wind microgrids in rural areas, providing real-time data on energy generation, storage, and consumption. The system should alert users to inefficiencies or maintenance needs.

Expected Outcome

A hardware-software prototype improving microgrid efficiency by 15%, with a mobile app for community operators to manage energy distribution.

Technical Feasibility

Employs affordable IoT sensors (e.g., Raspberry Pi, Arduino) and cloud-based analytics for real-time monitoring, compatible with existing microgrid setups.
AR-Based Cultural Heritage Preservation Platform
Organization: Government of Odisha
Category: Software | Theme: Heritage & Culture

Description

Create an augmented reality (AR) platform to digitize and preserve India's cultural heritage sites, allowing users to experience virtual tours with historical narratives. The platform should support low-bandwidth access for rural users.

Expected Outcome

An AR app with 3D models of at least five heritage sites, increasing tourist engagement by 10% and promoting cultural education.

Technical Feasibility

Uses AR frameworks (e.g., Unity, ARCore) with compressed 3D models for low-bandwidth compatibility, hosted on cloud servers.
Improved Onion storage technology for enhancing shelf life of onions
Organization: Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)
Category: Hardware | Theme: Agriculture FoodTech & Rural Development

Background

The majority of the farmers in India store the onion in naturally ventilated storage structures. In such structures, onions are exposed to extreme temperatures and relative humidity, which reduces the shelf life of onions and high storage loss. Improved onion storage technology will help in ensuring the availability of onions between seasons and obviate seasonal volatility in prices. The solutions could be in the form of improved, but cost-effective, materials and design of storage structures to manage temperature and relative humidity.

Description

Onion is a semi-perishable vegetable and is harvested during the rabi season, accounting for 65% of onion production, and hits the markets from April to May. The same crop must continue to meet the consumer demand till the month of October-November every year, before the kharif crop is harvested and brought to the market. It is therefore vital to store onions successfully in order to meet the regular supply. It is observed that nearly 30-40% of the crop is lost during storage due to various reasons in form of physiological weight loss, rotting, sprouting etc. In unexpected situations such as natural calamities, the losses even go beyond 50% creating heavy stress both on the demand and supply sides. The losses that occurred during storage are in terms of qualitative as well as in quantitative ways. Hence, it is imperative to take some crucial steps pertaining to onion storage with minimum losses to ensure an adequate supply to the market, thereby reducing the price fluctuations.

Various abiotic factors like temperature, relative humidity affect the health of onions; hence, their balance is essential to store the crop with minimum losses. Recent technological advancements (such as Design, Solar/Plasma/Irradiation energy, Nanotechnology, IoT including AI/ML) also offer an opportunity to provide infrastructure for onion storage and supply chain, besides value addition, even in rural India. Design innovations are pervading various sectors, including efficient onion storage at the farm level, rural as well as urban markets. Different process and product development advances will fetch better returns to farmers and competitive prices for consumers. More importantly, it will help in addressing the present as well as the impending challenges.

Expected Solution

Extension of shelf life with minimum loss.
Automated High-Current Short-Circuit Test System for MCB to comply with IEC 60898-1:2015
Organization: Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)
Category: Hardware | Theme: Smart Automation

Background

The safety of electrical installations hinges on reliable Miniature Circuit Breakers (MCBs). IEC 60898-1:2015 mandates rigorous short-circuit breaking capacity tests, crucial for ensuring MCBs perform correctly under severe fault conditions.

Existing Problem

Current manual or semi-automated testing methods for MCBs introduce significant challenges. These include imprecise R (resistive) and XL (inductive) circuit configurations, increased test times, and elevated safety risks for personnel during high-energy fault current generation (up to 10,000A). This impacts test accuracy, repeatability, and overall safety in the MCB certification process.

Detailed Description

This proposal outlines an automated machine to precisely control test currents, voltages, and circuit impedance, executing high-current short-circuit tests on single pole, SPN, DP, TP, and FP MCBs (0.5A-63A) per IEC 60898-1:2015. It features an Automated R and XL Circuit Combination Module with high-power, automatically switched banks for precise power factor control. A High-Current Power Source (transformer-based) delivers up to 10,000A. The Test Station includes universal MCB mounting and a critical arc chute for safety. A sophisticated Control and Data Acquisition System (PLC/Industrial PC) manages tests, captures high-speed waveforms, and analyzes data. A user-friendly HMI allows parameter input and automatic report generation. Comprehensive safety systems are integrated.

Expected Solution

The automated machine will perform MCB breaking capacity tests with unprecedented accuracy and repeatability, fully adhering to IEC 60898-1:2015. This automation will ensure precise parameter control, significantly reduce test times, and enhance safety by minimizing human intervention during high-energy fault conditions. This state-of-the-art facility will provide a reliable platform for MCB certification, contributing directly to electrical safety and quality assurance.