prof-freakenstein/SIH2025 huggingface dataset:
E tongue for Dravya identification
Organization: Ministry of Ayush
Category: Hardware | Theme: MedTech / BioTech / HealthTech

Background

In Ayurveda and other traditional medicine systems, Rasa (taste) is a fundamental criterion for identifying, classifying, and determining the therapeutic value of medicinal herbs. While this taste-based assessment has been effective for centuries, it is inherently subjective, relying on human perception and expert experience, which often leads to variability and inconsistency.

In the modern context, ensuring the authenticity, quality, and standardization of herbal raw materials is a critical challenge due to widespread adulteration, batch-to-batch phytochemical variations, and the global demand for reliable herbal products.

Recent advances in sensor science and analytical technologies provide innovative solutions to these challenges. Electronic tongue (e-tongue) systems can mimic human gustatory mechanisms by detecting complex chemical signatures, while spectroscopic techniques such as Near-Infrared (NIR), Raman, and UV-Visible spectroscopy offer rapid, non-destructive chemical profiling of herbal samples.

When integrated with Artificial Intelligence (AI) and Machine Learning (ML)—such as chemometric modelling, neural networks, and pattern recognition—large datasets can be analyzed to classify herbs based on taste profiles, threshold levels, and phytochemical composition with high precision. This AI-enabled, sensor-integrated approach ensures faster, objective, and cost-effective quality assurance, bridging traditional Ayurvedic principles with modern scientific validation.

Detailed Description

The problem statement aims to design and develop a sensor-integrated AI-enabled device for objective quality assessment of marketed herbal samples. The device will incorporate:

• A multi-sensor e-tongue array to detect basic taste modalities (sweet, sour, salty, bitter, pungent, astringent).
• A threshold detection module for quantifying the minimum perceptible concentration of each taste-related compound.
• Analytical integration with phytochemical profiling techniques (e.g., HPTLC, FTIR, LC-MS) for correlating taste signatures with chemical constituents.

Expected Solution

• A portable hardware prototype incorporating an electronic tongue (e-tongue) sensor array.
• Calibration using authenticated herb samples and machine learning models trained on large datasets of taste and phytochemical fingerprints.
• Smart alerts and decision support for detecting adulteration or substandard quality.
• A centralized or cloud-compatible database that updates continuously with new samples and learning outputs.
• Applicability across industries including Ayurvedic pharmacopeia standardization, herbal industry quality control, academic research, and regulatory bodies.
Smart Classroom & Timetable Scheduler
Organization: Government of Jharkhand
Category: Software | Theme: Smart Education

Background

Higher Education institutions often face challenges in efficient class scheduling due to limited infrastructure, faculty constraints, elective courses, and overlapping departmental requirements. Manual timetable preparation leads to frequent clashes in classes, underutilized classrooms, uneven workload distribution, and dissatisfied students and faculty members. With the increasing adoption of multidisciplinary curricula and flexible learning under NEP 2020, the class scheduling process has become more complex and dynamic, requiring intelligent and adaptive solutions.

Description

The current scheduling mechanism in most higher education institutes/colleges relies on manual input via spreadsheets or basic tools. These fail to account for real-time availability of faculty, room capacity, teaching load norms, subject combinations, and student preferences. A solution is required that will accommodate the various parameters required for scheduling classes for UG and PG students and return an optimized timetable ensuring:
• Maximized utilization of classrooms and laboratories
• Minimized workload on faculty members and students
• Achievement of required learning outcomes

Key Parameters

The following parameters can be taken into account as variables for creating optimized timetables:
- Number of classrooms available
- Number of batches of students
- Number of subjects to be taught in a particular semester
- Names of subjects
- Maximum number of classes per day
- Number of classes to be conducted for a subject per week / per day
- Number of faculties available for different subjects
- Average number of leaves a faculty member takes in a month
- Special classes that have fixed slots in timetable

Students may also consider additional variables that may help in effective timetable preparation.

Expected Solution

A web-based platform that can be linked to the college website. Authorized personnel will be able to login and input data against the listed variables to generate fully optimized timetables.

The platform should include:
• Login facility for authorized personnel to create and manage timetables
• Multiple options of optimized timetables to choose from
• Review and approval workflow for competent authorities
• Suggestions for suitable rearrangements when optimal solutions are not available
• Support for multi-department and multi-shift scheduling
Authenticity Validator for Academia
Organization: Government of Jharkhand
Category: Software | Theme: Smart Education

Background

With increasing digitization, the problem of fake degrees and forged academic certificates has become a major concern for higher education institutions, employers, and government bodies. Cases of fraudulent documents being used for jobs, admissions, or government schemes have highlighted the absence of a robust mechanism to verify the authenticity of educational credentials issued by colleges and universities.

At present, verification is often manual, relying on physical inspection, emails to institutions, or outdated databases. This creates delays, inconsistency, and susceptibility to corruption or manipulation. To preserve academic integrity and public trust, there is a pressing need for an efficient, secure, and scalable digital system to detect and prevent the use of fake degrees.

Detailed Description

The challenge is to create a digital platform that can authenticate and detect fake degrees or certificates issued by higher education institutions across Jharkhand. The system should be able to cross-verify uploaded documents (PDFs, scans, etc.) with institutional databases or credential registries, using metadata, QR codes, signatures, or embedded hashes.

Such a platform must work with both legacy certificates (issued before digitization) and new ones generated under university ERP systems. It should detect anomalies such as:
- Tampered grades or photos
- Forged seals or signatures
- Invalid certificate numbers
- Non-existent institutions or courses
- Duplicate or cloned documents

Incorporating AI, OCR (Optical Character Recognition), and blockchain or cryptographic validation, the platform should enable seamless certificate verification by employers, admission offices, scholarship agencies, and government departments. The goal is to create a trustable and publicly accessible system that protects institutions’ reputation and safeguards student achievements.

Expected Solution

A smart, scalable, and secure Fake Degree/Certificate Recognition system that includes:
• Upload interface for verifying entities (employers, institutions, agencies) to upload or input certificate details
• Certificate authenticity checker that:
   – Uses OCR to extract key details (name, roll number, marks, certificate ID)
   – Matches it against a verified database (centralized or decentralized)
   – Flags mismatches or formatting inconsistencies
• Digital watermark or blockchain verification support for newly issued certificates
• Institution integration module for universities/colleges to upload their certificate records in bulk or in real-time
• Admin dashboard for authorized bodies (e.g., Higher Education Department) to monitor verification activity, detect forgery trends, and blacklist offenders
• Alert system for invalid or forged entries
• Data privacy and access control measures to ensure secure handling of student information

This solution must be adaptable across different institutions, work with both physical and digital certificates, and be affordable for state-wide rollout.
AI-Based Crop Recommendation for Farmers
Organization: Government of Jharkhand
Category: Software | Theme: Agriculture FoodTech & Rural Development

Background

Farmers often face challenges in accessing timely, personalized, and accurate agricultural support. Language barriers, lack of technical knowledge, and limited reach of conventional advisory services reduce the effectiveness of existing solutions. Emerging generative AI technologies present an opportunity to deliver hyper-localized guidance in natural language, paired with visual understanding to assist with field-level problems like crop disease detection.

Detailed Description

The objective is to create an AI-driven decision support system that determines real-time soil properties (pH, moisture, nutrient content) based on satellite data (e.g., Soil Grids, Bhuvan APIs) or IoT sensors. The system must also account for localized weather forecasts, past crop rotation data to preserve soil fertility, and existing market demand and price trends obtained through APIs or scraping agri-market websites.

A machine learning model will provide the most appropriate crops for specified conditions, forecasting yield, profit margins, and sustainability scores. The solution should comprise a mobile application with a simple multilingual interface where farmers can enter or retrieve relevant data, see recommendations, and operate offline in low-connectivity regions.

Expected Solution

A mobile-based prototype offering farmers customized, science-guided crop advice, increasing income, making resources more efficient, and facilitating sustainable agriculture.
The solution should deliver an AI-powered platform that integrates text and image-based interactions tailored for agricultural use. It must support voice and chat interfaces in local languages, enabling farmers to ask questions and receive actionable responses.
Crowdsourced Civic Issue Reporting and Resolution System
Organization: Government of Jharkhand
Category: Software | Theme: Clean & Green Technology

Background

Local governments often face challenges in promptly identifying, prioritizing, and resolving everyday civic issues like potholes, malfunctioning streetlights, or overflowing trash bins. While citizens may encounter these issues daily, a lack of effective reporting and tracking mechanisms limits municipal responsiveness. A streamlined, mobile-first solution can bridge this gap by empowering community members to submit real-world reports that municipalities can systematically address.

Detailed Description

The system revolves around an easy-to-use mobile interface that allows users to submit reports in real-time. Each report can contain a photo, automatic location tagging, and a short text or voice explanation, providing sufficient context. These submissions populate a centralized dashboard featuring a live, interactive map of the city's reported issues. The system highlights priority areas based on volume of submissions, urgency inferred from user inputs, or other configurable criteria.

On the administrative side, staff access a powerful dashboard where they can view, filter, and categorize incoming reports. Automated routing directs each report to the relevant department such as sanitation or public works based on the issue type and location. System architecture accommodates spikes in reporting, ensuring quick image uploads, responsive performance across devices, and near real-time updates on both mobile and desktop clients.

Expected Solution

The final deliverable should include a mobile platform that supports cross-device functionality and seamless user experience. Citizens must be able to capture issues effortlessly, track the progress of their reports, and receive notifications through each stage — confirmation, acknowledgment, and resolution.
On the back end, a web-based administrative portal should enable municipal staff to filter issues by category, location, or priority, assign tasks, update statuses, and communicate progress. The platform should integrate an automated routing engine that leverages report metadata to correctly allocate tasks to departments.
A scalable, resilient backend must manage high volumes of multimedia content, support concurrent users, and provide APIs for future integrations or extensions. Lastly, the solution should deliver analytics and reporting features that offer insights into reporting trends, departmental response times, and overall system effectiveness — ultimately driving better civic engagement and government accountability.
Development of a Smart Digital Platform to Promote Eco & Cultural Tourism in Jharkhand
Organization: Government of Jharkhand
Category: Software | Theme: Travel & Tourism

Background

Jharkhand is blessed with natural beauty, tribal culture, historical landmarks, and eco-tourism hotspots like Netarhat, Patratu, Betla National Park, Hundru Falls, and Deoghar. However, despite its vast potential, the state's tourism industry remains underdeveloped due to a lack of digital infrastructure, limited promotional outreach, low tourist awareness, and unorganized travel and hospitality services.

Description

Tourists often find it difficult to access reliable information about destinations, local transportation, accommodations, cultural activities, and guides. Additionally, local artisans, tribal communities, and small-scale service providers who could benefit greatly from tourism remain largely excluded from the digital ecosystem. There is a strong need for a centralized digital platform that not only improves the tourist experience through authentic and accessible information but also connects and empowers local communities, making tourism in Jharkhand more inclusive, organized, and sustainable.

Expected Solution

Design and develop an AI-powered digital tourism platform (mobile app and/or website) for Jharkhand that offers:
• AI-based personalized itinerary planning and multilingual chatbot assistance for tourists.
• Blockchain-enabled secure transactions, guide verification, and digital certification for local service providers.
• Interactive maps and AR/VR previews of major tourist and cultural sites.
• Real-time transport and location info using geo-location.
• Integrated local marketplace for tribal handicrafts, events, homestays, and ecotourism.
• AI-driven feedback and sentiment analysis for continuous improvement.
• Analytics dashboard for tourism officials to monitor trends and impact.
AI-Based Smart Allocation Engine for PM Internship Scheme
Organization: Ministry of Corporate Affairs
Category: Software | Theme: Smart Automation

Background

The PM Internship Scheme enables students to gain industry exposure through structured internships. However, matching thousands of applicants with the most suitable opportunities remains a challenge, often leading to suboptimal selections and delays.

Description

The problem envisages a smart, automated system that uses AI/ML algorithms to match candidates with internship opportunities based on skills, qualifications, location preferences, and sector interests. The system should also account for affirmative action (e.g., representation from rural/aspirational districts, different social categories), past participation, and internship capacity of industries.

Expected Solution

A functional prototype with:
• AI-based matchmaking engine for internship placement
• A prototype of the front end demonstrating how this engine will work
AI-Based Internship Recommendation Engine for PM Internship Scheme
Organization: Ministry of Corporate Affairs
Category: Software | Theme: Smart Education

Background

The PM Internship Scheme receives applications from youth across India, including rural areas, tribal districts, urban slums, and remote colleges. Many of these candidates are first-generation learners with limited digital exposure and no prior internship experience. With hundreds of internships listed on the portal, it becomes difficult for such candidates to identify which ones match their skills, interests, or aspirations. This leads to misaligned applications and missed opportunities.

Description

The problem seeks to build a simple, lightweight AI-based recommendation engine that suggests the most relevant internships to each candidate based on their profile, academic background, interests, and location preferences. The system should be user-friendly, mobile-compatible, and work well even for users with low digital literacy. It should offer 3-5 personalized suggestions, instead of a long list, and help candidates make informed choices. The tool must be simple enough to be integrated with the existing PM Internship Scheme portal and must avoid complex or resource-intensive deployment.

Expected Solution

A functional prototype that:
• Captures basic candidate inputs (education, skills, sector interests, location)
• Uses a rule-based or ML-light model to suggest 3-5 top internships
• Has a simple, intuitive UI with minimal text and visual cues
• Can be accessed on mobile devices and adapted to regional language use
• Outputs recommendations in a clear, user-friendly format (e.g., cards or simple list)
Sentiment analysis of comments received through E-consultation module
Organization: Ministry of Corporate Affairs
Category: Software | Theme: Miscellaneous

Background

eConsultation module is an online platform wherein proposed amendments/draft legislations are posted on MCA's website for external users to submit their comments and suggestions pertaining to the same through the MCA21 portal. The comments are captured in a structured format for due consideration with respect to amending the draft legislation, based on the suggestions or observations received.

Problem Statement

The draft document soliciting comments is made available for a specified period, during which any stakeholder may submit their observations either on the overall amendment or on specific provisions of the draft legislation. In instances where a substantial volume of comments is received on draft legislation, there exists a risk of certain observations being inadvertently overlooked or inadequately analysed. In order to review each individual submission, leveraging AI-assisted tools will help ensure that all remarks are duly considered and systematically analysed. Requirement is the development of an AI model aimed at predicting the sentiments of the suggestions provided by stakeholders in the eConsultation module. It should also generate a visual representation in the form of a word cloud, highlighting the keywords utilised by the stakeholders within their suggestions.

Expected Outcome

The intention is to discern the feedback received from the stakeholders through the following:
• Sentiment analysis
• Summary generation
• Word cloud

The solution should considerably reduce the effort of the end user in analysing a high volume of comments. It should be able to clearly identify the sentiments of comments individually as well as broadly overall. The summary generation should be accurate and convey the meaning of the comment properly, in a precise manner. The word cloud feature should showcase the density of the words used by all users.
Development of Sensor for Detection Of Microplastics
Organization: Ministry of Earth Sciences (MoES)
Category: Hardware | Theme: Miscellaneous

Background

Microplastics — plastic particles less than 5 mm in size — have emerged as a global environmental threat, contaminating oceans, freshwater systems, and even drinking water. These particles originate from the degradation of larger plastics or from microbeads in consumer products. Their persistence and bioaccumulative nature pose serious risks to aquatic life, ecosystems, and potentially human health. Current methods for detecting microplastics — such as Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy — are often accurate but time-consuming, expensive, and laboratory-dependent, limiting their application for real-time, field-based monitoring.

Description

The project aims to develop a low-cost, efficient, and portable sensor capable of detecting microplastics in various water sources. The focus may be on using optical detection techniques — such as light scattering, fluorescence, or absorbance — combined with machine learning algorithms for pattern recognition. The sensor may be designed to identify microplastic particles based on their optical signatures, size distribution, and possibly polymer type. Water samples shall pass through a detection chamber, where embedded sensors capture spectral or visual data. This information would be processed in real time, either on-device or via a connected application. The system should be tested using standard microplastic samples (e.g., polyethylene, polypropylene) and validated against existing lab-based techniques.

Expected Solution

The expected outcome is a compact, field-deployable sensor system that can detect and quantify microplastics in water samples within minutes. It should offer reasonable sensitivity (down to 10–100 μm), operate on battery or solar power, and require minimal sample preparation. Additionally, the solution may include a companion software interface for data logging, visualization, and analysis. This innovation should have strong potential for adoption in microplastic monitoring, supporting researchers, environmental agencies, and water quality managers in making faster, data-driven decisions for pollution control and mitigation.
Development of a low-cost camera-based automated beach sand grain size mapping system
Organization: Ministry of Earth Sciences (MoES)
Category: Hardware | Theme: Miscellaneous

Background

Grain size is a fundamental property used to classify beach types and plays a critical role in influencing coastal morphodynamic processes. However, measuring grain size is particularly challenging, as it requires scientists to visit each site to collect sediment samples. These samples must then undergo time-consuming physical processing in the laboratory to determine their characteristics. Moreover, because beach sediments are dynamic and constantly changing due to wave action, tides, and weather events, this process must be repeated regularly to maintain accurate and up-to-date data.

Description

The above problem statement envisages that a low-cost camera-based automated mapping system be developed to estimate the sediment grain size distribution of the sandy beach area (i.e., berm, intertidal, and dune) and classify the beach category of the measurement region. A low-cost camera-based mapping system, along with automated prediction algorithms, is used to estimate the sediment grain size distribution of a particular area of interest to be developed. Also, this system should incorporate a GNSS/GPS receiver to map the geographic positions of the measurement location.

Expected Solution

A low-cost camera-based mapping system with a GNSS/GPS receiver should be developed with an automated image processing algorithm for beach sand grain size estimation/classification. Validation should be performed at least on one sample based on data collected in the area of interest.