SIH26109: Al-Based Predictive Modelling for Early Forecasting of Bovine Mastitis in lndian — SIH 2026 | Wynit

SIH26109: Al-Based Predictive Modelling for Early Forecasting of Bovine Mastitis in lndian — SIH 2026 | Wynit

What they'll ask you

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Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Exact machine learning algorithms or model architectures required?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific cloud infrastructure or database preferences?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Quantitative accuracy percentage required for successful validation?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

10 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for data integration engine. How would you build that?
Decoded from SIH26109 itself: Integrates data from sensors, farm management systems, laboratory records, and manual inputs. The brief asks for it by name.
The brief asks for ai prediction model. How would you build that?
Decoded from SIH26109 itself: Predicts mastitis risk at individual animal and herd levels 7-14 days before clinical signs appear. The brief asks for it by name.
The brief asks for hardware sensor prototype. How would you build that?
Decoded from SIH26109 itself: Includes IoT sensors for milk quality and wearable devices for body temperature and activity tracking. The brief asks for it by name.
The brief asks for mobile and web dashboard. How would you build that?
Decoded from SIH26109 itself: Provides real-time alerts, risk scores, GIS disease mapping, and multilingual user interfaces. The brief asks for it by name.
The brief asks for intervention recommender. How would you build that?
Decoded from SIH26109 itself: Suggests preventive and corrective actions for animal health management, hygiene, and nutrition. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check if the system can predict mastitis 7-14 days before clinical signs, integrate multi-source sensor and farm data, and function with a low-cost, rugged hardware and mobile setup under Indian field conditions.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (801 words)

• Background Bovine mastitis is one of the most prevalent and economically significant diseases affecting dairy cattle and buffaloes in lndia. The disease adversely impacts milk production, milk quality,animal health, and farm profitability, while also increasing treatment costs and antimicrobial usage. Despite its substantial economic and public health implications, mastitis is often detected only after clinical signs become apparent, limiting opportunities for timely intervention and prevention.The increasing adoption of digital dairy technologies, including automated milking systems,livestock monitoring devices, milk quality sensors, farm management softlvare, and environmental monitoring Systems, presents an opportunity to leverage Artificial lntelligence(Al), Machine Learning (ML), and Internet of Things (loT) technologies for early disease prediction and risk-based herd management. • Problem Statement Develop an integrated Al-enabled predictive forecasting system capable of identifying and predicting the risk of bovine mastitis at both individual animal and herd levels before the onset of clinical disease.The proposed solution should utilize real-time and historical farm data from multiple sources to generate early warning alerts, risk scores, and actionable recommendations for dairy farmers, veterinarians, dairy cooperatives, and animal health authorities. The system should support continuous monitoring, disease forecasting, and evidence-based decision-making to reduce disease incidence and associated economic losses. • Expected Solution The solution should be capable of: 1. Predicting mastitis risk at least 7-14 days before the appearance of clinical signs. 2. Generating animal-wise and herd-level risk assessments. 3. lntegrating data from sensors, farm management systems, laboratory records, and manual inputs. 4. Providing real-time alerts and notifications to farmers and veterinarians. 5. Continuously improving prediction accuracy through Al/ML-based learning models. 6. Supporting data-driven decision-making through user-friendly dashboards and visualization tools. 7. Recommending preventive and corrective interventions based on identified risk factors. 8. Supporting multilingual and mobile-enabled deployment for field-level adoption. • Data Parameters for Analysis The system should be capable of analysing and conelating multiple risk factors associated with mastitis occurrence, including: • Animal health and treatment records and herd strength • For individual level breed, age lactation number, disease history and vaccination status • Milk yield and milk quality parameters • Somatic Cell Count (SCC) and related indicators • Body temperature, aclivity levels, and rumination behaviour • Environmental, hygiene of the farm and climatic conditions • Feeding and nutritional practices • Housing conditions and farm management practices • Milking procedures and operational schedules • Previous disease history and co-morbidities • Worker hygiene and health-related risk factors Based on the analysis, the system may classify animals into risk categories such as: • No Risk • Low Risk • Moderate Risk • High Risk • Solution Components • Hardware Component- The hardware component may include: • loT-enabled sensors for monitoring milk conductivity, milk temperature, pH, milk yield,and other relevant indicators. • Wearable or collar-based devices for monitoring body temperature, udder surface temperature, activity, rumination, feeding behaviour, and physiological parameters. • Wireless communication through Bluetooth, W-Fi, GSM, NB-loT, LoRa, or equivalent technologies. • Battery-operated or solar-powered deployment suitable for field conditions. • GPS-enabled geo-tagging of animal and farm data. • Rugged, low-cost, and farmer-friendly designs suitable for lndian dairy production systems. • Software Component The software platform should include: • Al and machine learning models for mastitis risk prediction and forecasting. • Mobile applications for farmers, veterinarians, and field personnel. • Cloud-based data storage, integration, and analytics • Algorithms to predict subclinical mastitis with SCC. • Real-time herd health monitoring dashboards. • Early warning and notification systems through mobile alerts, SMS, or other communication channels. • Decision-support tools providing recommendations on animal health management, milking hygiene, nutrition, biosecurity, and veterinary interventions. • GIS-based visualization of disease trends, hotspots, and risk clusters. • Expected Deliverables • Functional prototype of the integrated mastitis forecasting system. • Al-based predictive analytics engine with demonstrated forecasting capability. • Mobile application and user interface for field deployment. • Cloud-based dashboard and data management platform. • Early warning and notification module. • Hardware prototype incorporating sensor-based data acquisition. • Demonstration and validation of predictive performance under field conditions. • Expected Outcomes and lmpact The successful solution is expected to: • Enable early detection and prevention of bovine mastitis both at individual level and herd level. • Reduce milk production losses and treatment costs. • lmprove milk quality, safety, and marketability. • Reduce indiscriminate antimicrobial usage and support antimicrobial resistance (AMR) mitigation efforts. • lmprove animal welfare, productivity, and longevity. • Promote precision livestock farming and digital dairy management. • Enhance the profitability and resilience of dairy farmers. • Contribute to the development of a data-driven livestock health surveillance ecosystem in lndia. • lnnovation Challenge The solution should be affordable, scalable, interoperable, and easy to deploy across diverse dairy production systems, including smallholder farms, dairy cooperatives, organized farms,and commercial dairy enterprises. Particular emphasis should be placed on low-cost implementation, ease of use, multilingual accessibility, data security, and predictive accuracy under lndian field conditions.