prof-freakenstein/SIH2025 huggingface dataset:
Smart Community Health Monitoring and Early Warning System for Water-Borne Diseases in Rural Northeast India
Organization: Ministry of Development of North Eastern Region
Category: Software | Theme: MedTech / BioTech / HealthTech

This problem statement proposes the development of a Smart Health Surveillance and Early Warning System that can detect, monitor, and help prevent outbreaks of water-borne diseases in vulnerable communities. The system can be:

• Collect health data from local clinics, ASHA workers, and community volunteers via mobile apps or SMS.
• Use AI/ML models to detect patterns and predict potential outbreaks based on symptoms, water quality reports, and seasonal trends.
• Integrate with water testing kits or IoT sensors to monitor water source contamination (e.g., turbidity, pH, bacterial presence).
• Provide real-time alerts to district health officials and local governance bodies.
• Include a multilingual mobile interface for community reporting and awareness campaigns.
• Offer dashboards for health departments to visualize hotspots, track interventions, and allocate resources.

Background

Water-borne diseases such as diarrhea, cholera, typhoid, and hepatitis A are prevalent in many rural areas and tribal belts of the Northeastern Region (NER), especially during the monsoon season. These outbreaks are often linked to contaminated water sources, poor sanitation infrastructure, and delayed medical response. The terrain and remoteness of many villages make it difficult for health workers to monitor and respond to emerging health threats in time.

Expected Solution

A digital health platform that includes:

• A mobile app for data collection and community health reporting.
• AI-based outbreak prediction engine using health and environmental data.
• Integration with low-cost water quality sensors or manual test kits.
• Alert system for health authorities and local leaders.
• Educational modules for hygiene awareness and disease prevention.
• Offline functionality and support for tribal languages.
Smart Tourist Safety Monitoring & Incident Response System using AI Geo-Fencing and Blockchain-based Digital ID
Organization: Ministry of Development of North Eastern Region
Category: Software | Theme: Travel & Tourism

This problem statement proposes the development of a Smart Tourist Safety Monitoring & Incident Response System that leverages AI, Blockchain, and Geo-Fencing technologies. The system should include:

Digital Tourist ID Generation Platform
• A secure blockchain-based system that issues digital IDs to tourists at entry points (airports, hotels, check-posts).
• These IDs should include basic KYC (Aadhaar/passport), trip itinerary, and emergency contacts, and be valid only for the duration of the visit.

Mobile Application for Tourists
• Auto-assign a Tourist Safety Score based on travel patterns and area sensitivity.
• Geo-fencing alerts when tourists enter high-risk or restricted zones.
• Panic Button with live location sharing to nearest police unit and emergency contacts.
• Optional real-time tracking feature (opt-in) for families and law enforcement.

AI-Based Anomaly Detection
• Detect sudden location drop-offs, prolonged inactivity, or deviation from planned routes.
• Flag missing, silent, or distress behaviour for investigations.

Tourism Department & Police Dashboard
• Real-time visualizations of tourist clusters and heat maps of high-risk zones.
• Access to digital ID records, alert history, and last known locations.
• Automated E-FIR generation for missing person cases.

IoT Integration (Optional)
• Smart bands or tags for tourists in high-risk areas (e.g., caves, forests).
• Continuous health/location signals and manual SOS feature.

Multilingual Support
• App and platform available in 10+ Indian languages and English.
• Voice/text emergency access for elderly or disabled travellers.

Data Privacy & Security
• End-to-end encryption and compliance with data protection laws.
• Blockchain ensures tamper-proof identity and travel records.

Background

In regions like the Northeast, where tourism is a key economic driver, ensuring the safety of visitors is paramount. Traditional policing and manual tracking methods are insufficient in remote and high-risk areas. There is a pressing need for a smart, technology-driven solution that ensures real-time monitoring, rapid response, and secure identity verification for tourists, while maintaining privacy and ease of travel.

Expected Solution

A robust digital ecosystem comprising:

• Web portal and mobile app for tourists and authorities.
• AI/ML models for behaviour tracking and predictive alerts.
• Blockchain-based ID generation and verification.
• Real-time dashboards for police/tourism departments.
• Optional IoT wearable integration for enhanced safety.
• Automated alert dispatch and evidence logging systems.
Low-Cost smart transportation solution for Agri produce from remote farms to nearest motorable road in NER Region
Organization: Ministry of Development of North Eastern Region
Category: Hardware | Theme: Transportation & Logistics

This problem statement proposes the design and development of prefabricated modular road panels that use bamboo reinforcement and plastic waste-infused composites to build durable, erosion-resistant roads in remote and hilly areas. The solution may be:

• Utilize bamboo mesh or strips as structural reinforcement within concrete or stabilized soil panels.
• Incorporate processed plastic waste (Low-Density Polyethylene - LDPE, High-Density Polyethylene - HDPE) into the mix to improve water resistance and flexibility.
• Include drainage features, slope adaptation mechanisms, and anti-slip surfacing.

Background

The Northeastern Region faces persistent challenges in building and maintaining rural roads due to difficult terrain, high rainfall, and limited access to conventional construction materials. Simultaneously, plastic waste management remains a growing concern in both urban and semi-urban pockets of the region. Bamboo, abundantly available in NER, offers high tensile strength and flexibility, while recycled plastic enhances water resistance and durability when used in road surfacing. Combining these two materials presents a unique opportunity to create sustainable, modular road infrastructure tailored for NER's needs.

Expected Solution

• A prototype modular panel system combining bamboo and recycled plastic.
• A deployment guide for constructing 100-200 meter stretches in rural terrain.
• Cost-benefit analysis compared to conventional road-building methods.
• Environmental impact assessment and scalability roadmap.
• Optional integration with IoT-based monitoring for wear and tear tracking.
Image based breed recognition for cattle and buffaloes of India
Organization: Ministry of Fisheries Animal Husbandry & Dairying
Category: Software | Theme: Agriculture FoodTech & Rural Development

Background

The Government of India is implementing the Bharat Pashudhan App (BPA) for systematic data recording of breeding, health, and nutrition of dairy animals. Field Level Workers (FLWs) are responsible for capturing animal data on the ground. However, despite multiple training programs, a recurring issue is the incorrect identification and registration of animal breeds of cattle and buffaloes. This misclassification significantly affects the integrity and usability of the data for research, policy planning, and targeted interventions.

Description

Breed identification errors in BPA often arise due to manual judgment and lack of breed-specific awareness among FLWs. India, being home to a diverse array of indigenous and crossbred cattle and buffalo breeds, presents a complex challenge for accurate breed identification. Incorrect entries compromise the value of collected data and, in turn, impact the effectiveness of genetic improvement, nutrition planning, disease control, and overall program outcomes.

To address this, an AI-driven solution that can identify the breed of an animal using its image can prove extremely valuable. By using image recognition and machine learning techniques, the software can standardize breed identification and minimize manual errors. If successfully developed and validated, such a system can be integrated with the BPA to act as a decision-support tool for FLWs during registration.

Expected Solution

• Uses Artificial Intelligence (AI) and image analysis to recognize and classify the breed of cattle and buffaloes based on images.
• Can handle diverse environmental backgrounds, lighting conditions, and varying animal poses.
• Maintains a breed database (for the most common Indian cattle and buffalo breeds and their crosses).
• Provides breed suggestions or confirmations at the time of registration in BPA.
• Can be seamlessly integrated with the BPA platform to support real-time validation or correction of breed entries.
• Includes a user-friendly interface for FLWs with minimal technical training requirements.
Image based Animal Type Classification for cattle and buffaloes
Organization: Ministry of Fisheries Animal Husbandry & Dairying
Category: Software | Theme: Agriculture FoodTech & Rural Development

Background: In dairy farming, evaluating the body structure of animals is vital to predict their longevity, productivity, and reproductive efficiency. Traditionally, Animal Type Classification (ATC)—which involves scoring animals for physical traits—is conducted by trained personnel through visual inspection and manual measurement of specific body parts. However, this manual method is prone to human error and subjectivity, even with trained professionals, resulting in inconsistent and potentially unreliable data.

With advances in Artificial Intelligence (AI) and image processing technologies, there is an opportunity to automate this process. Automated scoring based on images can ensure standardization, minimize observer bias, and improve the reliability of data captured for scientific and breeding purposes.

Description: The Government of India is implementing the Rashtriya Gokul Mission (RGM) since December 2014, aiming to conserve and develop indigenous bovine breeds, genetically upgrade the bovine population, and enhance milk productivity. Under this mission, Progeny Testing (PT) and Pedigree Selection (PS) programs are being carried out in key dairy breeds across the country to produce high genetic merit bulls for breeding purposes.

Animal Type Classification (ATC) is a crucial step in identifying top-performing elite dams, which are potential mothers of future breeding bulls. Currently, ATC is performed manually by a trained Animal Typer who visually examines and measures physical traits, and then records the scores in the Bharat Pashudhan App (BPA). Despite training, errors due to fatigue, bias, or measurement inaccuracies can adversely affect data quality and scientific analysis.

There is a need for an AI-driven solution that can automate this classification process by analyzing animal images, extracting body structure parameters, and assigning standardized scores with minimal human intervention. If integrated with BPA, such a solution would enhance the accuracy, efficiency, and scientific validity of animal evaluation under PT and PS programs.

Expected Solution: Students are expected to develop an AI-based Auto Recording of Animal Type Classification System that can:

Use images of cattle and buffaloes to evaluate physical traits relevant to Animal Type Classification.
Extract and quantify specific body structure parameters (e.g., body length, height at withers, chest width, rump angle, etc.) using AI and image processing techniques.
Generate objective and consistent classification scores.
Auto-record and store the classification data in a structured format.
Provide seamless integration with BPA to auto-save the classification records at the time of evaluation.
Be user-friendly and operable by field personnel with minimal technical skills.
Development of a Digital Farm Management Portal for Implementing Biosecurity Measures in Pig and Poultry Farms
Organization: Ministry of Fisheries Animal Husbandry & Dairying
Category: Software | Theme: Agriculture FoodTech & Rural Development

Background

Biosecurity is a cornerstone of animal health management, particularly in the pig and poultry sectors, where disease outbreaks such as Avian Influenza and African Swine Fever can cause significant economic losses, threaten food security, and disrupt rural livelihoods. Despite its importance, many farmers—especially smallholders in resource-limited areas—struggle to access practical, actionable information on biosecurity protocols, risk assessment tools, and regulatory compliance requirements.

Problem Description

There is an urgent need for a user-friendly, digital platform that empowers farmers to implement, monitor, and sustain robust biosecurity practices on their farms. This portal should offer end-to-end solutions for farm-level biosecurity management by integrating:

• Customizable risk assessment tools based on local epidemiological conditions.
• Interactive training modules and best practice guidelines tailored for pig and poultry production systems.
• Compliance tracking features aligned with regulatory frameworks to help farmers work toward disease-free compartment recognition.
• Real-time alerts and monitoring dashboards for disease outbreaks and biosecurity breaches.
• Multilingual and mobile-first design to ensure accessibility in remote and rural areas.

The platform should also enable data collection and analysis for policy support, foster collaborative networking among stakeholders (farmers, veterinarians, extension workers, etc.), and promote long-term resilience and sustainability in the livestock sector.

Expected Outcomes

• Enhanced farmer awareness and education on biosecurity.
• Improved risk management at the farm level as well as self-assessment.
• Easy access to customized biosecurity protocols and guidelines.
• Digital record-keeping and compliance tracking.
• Timely alerts and disease notifications to farmers.
• Healthier livestock and increased farm productivity.
• Empowerment of small and marginal farmers with limited resources.
• Support to authorities in data-driven surveillance and policy making.
• Stronger collaboration across the livestock ecosystem.
• Improved national preparedness for zoonotic and transboundary diseases.
Development of a Digital Farm Management Portal for Monitoring Maximum Residue Limits (MRL) and Antimicrobial Usage (AMU) in Livestock
Organization: Ministry of Fisheries Animal Husbandry & Dairying
Category: Software | Theme: Agriculture FoodTech & Rural Development

Background

Antimicrobials are vital in ensuring animal health and productivity in modern livestock systems. However, inappropriate and excessive use of these drugs can result in antimicrobial residues in animal-derived food products and contribute to the rise of antimicrobial resistance (AMR)—a major global threat to both animal and public health. Monitoring antimicrobial usage (AMU) and ensuring compliance with Maximum Residue Limits (MRL) is crucial to promoting responsible drug use and safeguarding food safety.

Problem Description

There is a growing need for a digital solution that can help track, monitor, and manage antimicrobial usage across livestock farms while ensuring adherence to MRL standards. The proposed system should serve as a centralized digital portal that facilitates:

• Recording and tracking the types, dosages, frequency, and reasons for antimicrobial use in animals both in treatment and through feed.
• Integration with veterinary prescriptions and treatment logs to monitor compliance.
• Alert systems for withdrawal periods and MRL compliance prior to the sale or processing of animal products.
• Real-time dashboards and data visualization tools for analysis of AMU trends across species, regions, and time periods.
• Use of blockchain or other secure technologies to ensure data integrity and traceability.
• Mobile app interfaces for easy data entry by farmers and veterinarians in field conditions.

This digital platform would contribute to improved antimicrobial stewardship, better regulatory enforcement, and help India align with global AMR mitigation strategies, while also strengthening public trust in livestock products.

Expected Outcomes

• Improved antimicrobial stewardship practices at the farm level.
• Enhanced compliance with MRL norms and regulatory frameworks.
• Real-time availability of data on AMU for authorities and stakeholders.
• Data-driven decision-making to inform policy and farm-level action.
• Capability for trend analysis and automated reporting.
• Healthier livestock and reduction in antimicrobial residues in food.
• Improved public health protection and consumer confidence.
• Contribution to global AMR reduction efforts.
• Better engagement of farmers and veterinarians through digital tools.
• Promotion of sustainable and responsible livestock farming practices.
Disaster Preparedness and Response Education System for Schools and Colleges
Organization: Government of Punjab
Category: Software | Theme: Disaster Management

Problem Description

In India, schools and colleges are often unprepared for natural disasters such as earthquakes, floods, and fires. While emergency guidelines exist on paper, there is a lack of structured disaster management education integrated into the curriculum. Institutions lack digital tools to simulate disaster scenarios or conduct virtual drills to train students and staff on safety protocols.

Furthermore, there’s a gap in localized awareness—many students are unaware of how to react during disasters specific to their region. Manual drills, where they occur, are infrequent and often poorly coordinated, failing to instill practical preparedness.

Impact / Why this problem needs to be solved

Lack of awareness and preparedness leads to panic, chaos, and potentially fatal outcomes during emergencies. By integrating disaster education into regular learning, institutions can equip students and staff with life-saving knowledge and skills. This is especially critical in areas prone to natural calamities.

Empowering young people with this knowledge not only makes campuses safer but also contributes to a more disaster-resilient society.

Expected Outcomes

• A digital platform or app that offers interactive disaster education modules, region-specific alerts, and virtual drills.
• Gamified learning experiences to improve engagement.
• Emergency contact directories and real-time communication tools during disasters.
• Dashboards for school administrators to track preparedness scores and drill participation.

Relevant Stakeholders / Beneficiaries

• Students (K-12 and higher education)
• Teachers and administrative staff
• Educational institutions and local disaster response teams
• Parents and guardians
• Government departments (NDMA, Education Ministry)

Supporting Data

• NDMA reports show low awareness levels in schools despite India’s high disaster vulnerability index.
• UNDRR has recommended integrating disaster risk reduction in education policies (Ref: National Disaster Management Authority).
Gamified Environmental Education Platform for Schools and Colleges
Organization: Government of Punjab
Category: Software | Theme: Smart Education

Problem Description

Despite the rising urgency of climate change and environmental degradation, environmental education remains largely theoretical in many Indian schools and colleges. Students are often taught textbook-based content with little emphasis on real-world application, local ecological issues, or personal responsibility.

There is a lack of engaging tools that motivate students to adopt eco-friendly practices or understand the direct consequences of their lifestyle choices. Traditional methods fail to instill sustainable habits or inspire youth participation in local environmental efforts.

Impact

As future decision-makers, students must be environmentally literate and empowered to take meaningful actions. Without innovative education methods, we risk raising a generation unaware of sustainability challenges.

An interactive, practical approach to environmental learning will foster long-term behavioral change, local involvement, and a ripple effect across families and communities. This aligns with India's SDG goals and NEP 2020's emphasis on experiential learning.

Expected Outcomes

• A gamified mobile/web platform or app that teaches students about environmental issues through interactive lessons, challenges, quizzes, and real-world tasks (e.g., tree-planting, waste segregation).
• Tracking of eco-points, enabling school-level competitions.
• Rewards for sustainable practices through digital badges and recognition.

Relevant Stakeholders / Beneficiaries

• School and college students
• Teachers and eco-club coordinators
• Environmental NGOs and government departments

Supporting Data

• UNESCO reports that experiential, gamified learning increases student retention and engagement by over 70%.
• NEP 2020 encourages integration of environmental awareness into the curriculum.
Smart Crop Advisory System for Small and Marginal Farmers
Organization: Government of Punjab
Category: Software | Theme: Agriculture FoodTech & Rural Development

Problem Description

A majority of small and marginal farmers in India rely on traditional knowledge, local shopkeepers, or guesswork for crop selection, pest control, and fertilizer use. They lack access to personalized, real-time advisory services that account for soil type, weather conditions, and crop history.

This often leads to poor yield, excessive input costs, and environmental degradation due to overuse of chemicals. Language barriers, low digital literacy, and absence of localized tools further limit their access to modern agri-tech resources.

Impact / Why this problem needs to be solved

Helping small farmers make informed decisions can significantly increase productivity, reduce costs, and improve livelihoods. It also contributes to sustainable farming practices, food security, and environmental conservation. A smart advisory solution can empower farmers with scientific insights in their native language and reduce dependency on unreliable third-party advice.

Expected Outcomes

• A multilingual, AI-based mobile app or chatbot that provides real-time, location-specific crop advisory.
• Soil health recommendations and fertilizer guidance.
• Weather-based alerts and predictive insights.
• Pest/disease detection via image uploads.
• Market price tracking.
• Voice support for low-literate users.
• Feedback and usage data collection for continuous improvement.

Relevant Stakeholders / Beneficiaries

• Small and marginal farmers
• Agricultural extension officers
• Government agriculture departments
• NGOs and cooperatives
• Agri-tech startups

Supporting Data

• 86% of Indian farmers are small or marginal (NABARD Report, 2022).
• Studies show ICT-based advisories can increase crop yield by 20–30%.