Hackathon Hub

An AI-supported weed management rover is a robotic vehicle using computer vision and machine learning to identify and remove weeds selectively, preventing crop damage and reducing herbicide use in fields. Equipped with cameras, Raspberry Pi processing, and tools like cutters or lasers, it navigates autonomously between crop rows for precise intervention. Ideal for vegetable fields in Theni, it targets invasive species like congress grass while promoting sustainable farming.

Design and implement an AI-driven condition monitoring system specifically trained to detect and predict faults in distribution transformers subjected to EV charging loads, utilizing advanced sensing (e.g., dissolved gas analysis, thermal imaging) and data analytics

low-cost IoT-based healthcare monitoring ecosystem that uses smart hardware devices to continuously track vital health parameters and enable early medical intervention in rural areas.

This project aims to develop an advanced drone-based system for accurate and efficient marking of construction site columns, addressing the limitations of conventional manual marking methods. The proposed system integrates computer vision, AI-based image processing, and autonomous flight control to precisely identify and mark column locations according to design layouts. It ensures high accuracy, reduced human error, and improved site safety, even under challenging environmental conditions such as variable lighting and weather. The system will be designed to seamlessly integrate with existing construction workflows and digital models (like BIM), allowing for realtime data synchronization and efficient project execution. By automating the column marking process, the project seeks to minimize labor costs, accelerate project timelines, and enhance overall construction quality and reliability.

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.

AI-driven ecosystem to detect embedded malware and trojans in power sector hardware devices by analyzing firmware, device behavior, and network patterns. The power sector increasingly depends on advanced hardware devices such as smart meters, PLCs, sensors, routers, and SCADA systems for monitoring and controlling critical infrastructure. These devices often operate with embedded firmware and complex hardware components sourced from multiple vendors, making them vulnerable to hidden malware, trojans, or backdoors at the hardware or firmware level. Traditional cybersecurity mechanisms primarily focus on software and network threats and are often inadequate in detecting malicious components embedded within hardware devices. Such hidden threats can compromise the integrity of power systems, disrupt grid operations, manipulate data, and pose serious risks to national security and public safety. There is a pressing need for a robust and intelligent framework that can detect, analyze, and prevent embedded malware and trojans in hardware devices used in the power sector. By integrating hardware-level monitoring, firmware analysis, AI-driven anomaly detection, and blockchain-based integrity verification, the proposed solution aims to enhance trust, transparency, and resilience in critical power infrastructure.

Build strong robots that can go into fire zones where humans cannot safely enter. These robots will detect fire, move inside dangerous areas, and help in firefighting and rescue, reducing risk to human firefighters.

Water bodies such as rivers, lakes, and reservoirs play a critical role in ecosystems and human life. However, their health is continuously threatened by pollution, climate change, and human activities. Current water quality monitoring methods rely on manual sampling or fixed stations, which are limited in coverage, expensive, and incapable of providing continuous real-time data. There is a need for an autonomous hardware device that can continuously collect environmental data from water bodies without relying on external power sources. The device should generate its own power using renewable energy available in the environment, such as water flow, solar energy, or wave motion. Additionally, the device must be designed to operate safely in dynamic water environments by preventing collisions with obstacles and resisting tampering or physical damage. It should securely transmit real-time sensor data to remote systems or mobile applications for analysis and decision-making. The proposed solution aims to develop a robust, self-powered, collision-resistant, and tamper-proof floating or submerged hardware system capable of continuously monitoring water health parameters and transmitting reliable real-time data.

The soldier strap will be given to soldiers and it will have the following functionality. 1) GPS location of the soldier. 2) Will monitor and record maximum number of vital body parameters. 3) SoS facility. 4) Sufficient Battery Life. 5) Should support location timeline. Security becomes very important in such straps. Such a strap should transfer information by using a MANET-type communication system. It should access public network only when no node is available in the immediate vicinity and only in case of emergency. Take the liberty to mores sustainable and useful solution.

Open Innovation

Farmers often lack access to real-time market trends, financial schemes, and government support information, which affects their decision-making and income potential. The objective is to build a smart digital platform that delivers live market insights, personalized scheme recommendations, and simplified financial assistance access to empower farmers and enhance agricultural productivity and profitability.

Urban areas face rising road accidents and inefficient emergency response due to poor driving behavior monitoring and delayed incident detection. The objective is to design an intelligent system that improves driver safety, monitors behavior, and enables faster emergency response for accidents using AI and real-time monitoring technologies.

Rising mental health issues among youth are often undetected due to lack of real-time monitoring tools. Drug and alcohol consumption patterns can act as early warning signals. The objective is to build a predictive AI system that analyzes behavioral patterns, substance usage, and lifestyle signals to detect early symptoms of depression and anxiety, enabling timely intervention and support.

The rapid spread of fake news, deepfakes, and manipulated digital content threatens public trust and social harmony. The objective is to create an AI-powered verification ecosystem that analyzes textual content, multimedia data, and manipulated images to detect misinformation and prevent its propagation across digital platforms.

Cyber incidents generate large volumes of complex log data, making manual analysis inefficient and slow. The objective is to develop an AI-driven digital forensic ecosystem capable of ingesting, correlating, and analyzing massive security logs to help investigators identify threats faster and improve incident response accuracy.

Younger generations are becoming disconnected from India’s cultural, historical, and ecological heritage due to static and unengaging learning platforms. The objective is to design an immersive, gamified, and AI-driven digital platform that promotes Indian culture, folk traditions, biodiversity, and regional knowledge through interactive storytelling and participatory experiences.

Deaf and Hard-of-Hearing individuals face communication barriers in public services, healthcare, and education due to limited interpreter availability. The objective is to develop a real-time sign language interpretation application that bridges communication gaps, promotes accessibility, and ensures inclusive participation in social and institutional interactions.

Passengers often struggle to navigate large railway stations due to complex layouts and lack of accessible guidance. The objective is to develop a smart navigation system with real-time directions, accessibility support, digital maps, and multi-platform availability to improve passenger convenience and mobility.

MSMEs face administrative burdens in managing payroll records and complying with multiple labor law filing requirements. The objective is to develop a freeware, open-source payroll software that automates payroll data management and generates legally compliant registers, reports, and returns for labor regulations.

Criminal exploitation through digital platforms is increasing, posing severe risks to women and children. The objective is to design an AI-driven cyber monitoring system that identifies suspicious online activities, predicts potential threats, and alerts authorities to prevent crimes proactively.

Coal mining operations generate significant carbon emissions with limited digital tools available for monitoring and reduction strategies. The objective is to develop a web-based platform that calculates emissions, evaluates carbon offset mechanisms, and suggests sustainable pathways toward carbon neutrality.

Academic institutions rely on faculty publications for ranking and accreditation, but data collection from multiple research platforms is manual and error-prone. The objective is to build an automated system that collects, validates, organizes, and generates structured publication reports from various academic databases.

Student dropout rates remain high due to socioeconomic challenges, lack of support systems, and insufficient early detection mechanisms. The objective is to develop integrated AI-based solutions that predict dropout risks, provide personalized learning support, and strengthen communication between schools, parents, and communities.

Cloud platforms face increasing Distributed Denial of Service (DDoS) attacks, causing service disruptions and security risks. The objective is to build a blockchain-backed AI-based security system that detects DDoS attacks in real time, securely stores threat intelligence, and enables coordinated cloud defense strategies.

Maritime systems lack collaborative intelligence, secure data sharing, and resilience during communication failures. The objective is to design a swarm-based maritime platform using AI agents and blockchain to enable autonomous coordination, threat detection, and self-healing infrastructure capabilities.

B2B RFP processes are time-consuming, fragmented, and heavily manual, reducing organizational efficiency and proposal success rates. The objective is to develop a multi-agent AI system that automates RFP discovery, requirement analysis, technical mapping, pricing estimation, and final proposal generation.

Designing Customer 360 data products in banking involves complex manual collaboration across teams and systems. The objective is to create an agentic AI system that interprets business requirements, designs structured data models, identifies source attributes, generates mappings, and standardizes data product certification workflows.

Locating missing aircraft is difficult due to vast search areas and limited predictive tools. The objective is to develop software that analyzes flight data and calculates probable crash zones to reduce search time and improve rescue operation efficiency.

Cyberbullying is evolving with subtle harassment methods such as sarcasm, coded language, and emotional manipulation. The objective is to build an advanced AI system that detects harmful interactions across public posts and private chats while ensuring privacy and accurate risk assessment.

E-commerce platforms face sophisticated fraud techniques including account takeovers and fake transactions. The objective is to design an AI-driven real-time fraud detection system that monitors user behavior, flags suspicious activity, and prevents unauthorized transactions through automated risk mitigation.

Construction projects frequently suffer from material shortages, workforce mismanagement, and unexpected disruptions. The objective is to build a predictive analytics platform that optimizes logistics, labor scheduling, and project planning to minimize delays and improve operational efficiency.

Rapid urbanization leads to inefficient placement of utilities and public services due to fragmented planning approaches. The objective is to develop a GIS-based intelligent planning system that analyzes spatial data and recommends optimal infrastructure placement for sustainable city development.

Traditional sentiment analysis tools fail to detect sarcasm, emotional nuances, and contextual expressions in social media content. The objective is to create an advanced AI-driven system that performs multi-level sentiment detection and emotion analysis to deliver deeper business insights.

Investigators face challenges retrieving digital evidence from encrypted or damaged devices. The objective is to develop an AI-powered forensic platform that supports authorized data recovery, evidence reconstruction, and automated legal reporting while maintaining ethical and privacy standards.

Complex surgical procedures require coordination of multiple robotic instruments, which is difficult for a single surgeon to manage. The objective is to build a multi-agent robotic surgical system where AI-controlled instrument agents coordinate autonomously while ensuring safety, resilience, and real-time surgical precision.

Recurring floods in the Indo-Gangetic plains destroy 20–30% of heirloom crop seeds annually, threatening indigenous biodiversity, food security, and culturally significant farming traditions. Existing centralized seed banks fail to protect hyper-local varieties adapted to micro-climates and community rituals. The objective is to develop a decentralized, blockchain-secured “living seed vault” network that integrates satellite-based flood prediction, AI-driven genomic phenotyping, computer vision-based seed trait analysis, and oral history transcription. The system should create a searchable bio-cultural genome atlas, score flood resilience using ML models, and enable community-owned micro-vaults powered by smart contracts, NFTs, IoT alerts, and drone-based evacuation triggers during high-risk flood windows.

India’s gig delivery riders experience significantly higher accident rates due to poor road conditions, low visibility, and fatigue—especially in low-signal peri-urban areas where traditional SOS apps fail. The objective is to build a lightweight, offline-first mesh-networked SOS mobile application that uses phone sensors (GPS, accelerometer) to detect hazards and fatigue in real time. The system should enable peer-to-peer Bluetooth/WiFi Direct alert sharing within a 1 km radius, auto-escalate emergencies to family or authorities with breadcrumb tracking, and crowdsource hazard data for safer delivery ecosystems.

Over 200+ traditional handloom weaving styles in our country are declining due to the absence of apprentices and lack of interactive digital preservation tools. Tacit knowledge such as thread tension and motif evolution cannot be transmitted effectively through static documentation. The objective is to build an AR-powered mobile application that overlays “ghost artisan” holographic guides onto real loom setups, enabling real-time skill replication using ARKit/ARCore. The system should incorporate gesture-based learning, pattern evolution features, NFT-like digital craft tokens, and offline community upload capabilities to preserve and modernize traditional weaving practices.

Engineering college face inefficient bus utilization and long commute times due to uncoordinated routes and lack of real-time demand forecasting. This results in overcrowded buses, empty return trips, and operational inefficiencies. The objective is to build a crowd-sourced shuttle demand prediction dashboard that collects rider intent through QR scans or SMS, generates heatmaps of demand spikes, clusters trips within time windows, and dynamically suggests route merges to optimize fleet usage. The system should notify drivers via WhatsApp or SMS to reroute based on predictive demand insights.

Open Innovation