SIH26111Software
Smart Al-Enabled Rapid Feed and Silage Quality Testing System for Dairy Farmers
Ministry of Fisheries, Animal Husbandry & Dairying
Official Description
• Background Animal nutrition directly affects milk production, animal health, reproductive performance, and dairy profitability. Dairy farmers often face challenges due to poor-quality cattle feed,adulterated feed ingredients, fungal contamination, toxin presence, and low-quality silage.Conventional feed testing laboratories are expensive and inaccessible for many rural farmers.There is a need for rapid, portable, affordable, and digitally enabled feed quality assessment systems.Emerging technologies such as Al, loT, spectroscopy, computer vision, and biosensors can help create real-time feed testing and advisory systems for dairy farmers.
• Description Participants are required to develop a rapid digital testing solution capable of:
• Assessing nutritional quality of cattle feed and silage;
• Detecting adulteration and contamination;
• Providing instant farmer advisories and feed recommendations;
• Monitoring feed storage and silage conditions.
The solution may include:
• Portable testing devices;
• Smartphone-enabled feed analysis;
• Al-powered nutritional prediction;
• Cloud dashboards;
• QR-based authenticity systems.
The system may detect:
• Crude protein
• Moisture
• Fiber
• Energy value
• Mineral deficiencies
• Urea adulteration
• Sand/silica contamination
• Aflatoxins and mycotoxins
• Fungal contamination Silage monitoring may include:
• pH
• Fermentation quality
• Moisture
• Spoilage indicators
• Mould growth
• Expected Solution The expected solution should:
• Provide testing results within minutes;
• Be low-cost and portable;
• Support multilingual farmer interfaces;
• Work offline in rural areas;
• Generate nutritional and storage advisories;
• Enable cloud-based monitoring and traceability.
• Expected technologies
• AI/ML
• loT sensors
• NIR spectroscopy
• Mobile applications
• Computer vision
• Cloud analytics
• Predictive advisory systems
• Insert Table Here*
Official Hackathon Facts
Theme / DomainAgriculture, FoodTech & Rural Development
Track TrackSoftware
Submission Deadline20 September 2026
Ideas Registered0/500
✨ SIH Fit Analysis
Estimated DifficultyAdvanced
Social Impact Score (3/5)
🏥 Healthcare🌱 Sustainability🚀 Advanced Technology
Primary Tech AreaComputer Vision
Technology Stack Tags
AI/MLComputer VisionPredictive AnalyticsRecommendation SystemsSensorsCloudWeb DevelopmentMobile Development
Suggested Skills
PythonOpenCVTime-series modellingRecommendation / ranking methodsSensor interfacingAWS/Azure/GCPReact / Node.jsFlutter / React Native
✦ Why This Problem is Interesting
Combines Computer Vision, Predictive Analytics with the problem domain, creating a technically focused solution opportunity.
* Note: SIH Fit Analysis contains derived ratings and classification schemas generated to aid team selection; these are not official ratings from the Smart India Hackathon organizers.
