An integrated AI-enabled predictive forecasting system with hardware sensors and a mobile app that helps dairy farmers and veterinarians predict bovine mastitis 7-14 days early.
Ministry of Fisheries, Animal Husbandry & Dairying · Department of Animal Husbandry & Dairying · Hardware
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Decomposed from what the description asks for. Nothing added.
Data Integration Engine
Integrates data from sensors, farm management systems, laboratory records, and manual inputs.
AI Prediction Model
Predicts mastitis risk at individual animal and herd levels 7-14 days before clinical signs appear.
Hardware Sensor Prototype
Includes IoT sensors for milk quality and wearable devices for body temperature and activity tracking.
Mobile and Web Dashboard
Provides real-time alerts, risk scores, GIS disease mapping, and multilingual user interfaces.
Intervention Recommender
Suggests preventive and corrective actions for animal health management, hygiene, and nutrition.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
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.
A jury can still ask about these. Decide them deliberately rather than by accident.
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.
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.
Each one is quoted from a gap in the brief, not a guess about your team.
Needs physical hardware
Listed as a hardware problem statement, so a working demo needs physical components you have to source yourself.
No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
Needs data you may not get
The description requires historical farm data, somatic cell counts, and health records across diverse risk factors that students cannot easily obtain.
No measurable target
While it asks for prediction accuracy under field conditions, it never states a mandatory percentage threshold for true positives or false alarms.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
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.