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SMART INDIA HACKATHON 2025 • Problem Statement ID - 25005 • Problem Statement Title - lmage based Animal Type Classification for cattle and buffaloes • Theme - Agriculture, FoodTech & Rural Development • PS Category - Software • Team ID - 99919 • Team Name - SkyNRG
2 @SIH Idea submission- Template GauDrisht i Our Solution Innovation & Uniqueness How it addresses the problem Problem Solution Problem Overview Evaluating AI performance across different animal breeds is challenging due to variations in detection precision, segmentation accuracy, and score consistency. This study analyzes breed- wise performance to identify gaps and ensure more reliable, fair, and generalizable models. GPS tagging and timestamping of evaluations Voice Summaries For User-Friendly Use Archival System Secure record keeping Auto Breed & Animal ID AI driven recognition Unified API Layer standardized ATC data access Bulk Uploads Fast data handling Animal Segmentation & Analysis Continuous Learning Loop Improves with human feedback Mobile- Friendly UI Manual ATC is subjective & error-prone Data entry is slow & fragmented Limited accessibility for field staff No proof of evaluation authenticity Accuracy does not improve over time AI-based automated scoring for consistency Unified API + bulk upload + auto-recording Voice summaries, multi-language, mobile-friendly UI GPS tagging & timestamping of Evaluation Continuous learning loop from human feedback PDF Exports of Analysis Reports AI-Driven Animal Evaluation – Automatically identifies breed, checks image quality, detects landmarks, and measures key body traits for ATC scoring. Interactive & Accessible Reports – Annotated images, confidence scores, multilingual support, voice summaries, and cross-questioning chat for better usability. Seamless Data Management – Bulk image processing, PDF exports, local archives, and GPS- tagged records for easy storage and sharing. SkyNRG
2 @SIH Idea submission- Template Global Practices & Technology Landscape United State (Holstein USA) • Uses Linear Type Classification with ~17 structural traits • Scores on a 1–50 scale • Digital tools for fast data entry, but evaluation remains manual & subjective • Strong breeding databases; limited automation SkyNRG Netherlands (CRV) • Traditional linear scoring system • Industry leaders in 3D imaging, digital phenotyping, and AI-based cow morphology • Pilot programs exist but not deployed nationwide • Closest global ecosystem to automated ATC Canada (Holstein Canada) • 22-trait structured scoring with strict evaluator certification • Highly scientific trait definitions and composite indices • Active research in computer vision & automated conformation analysis • Still field-dependent on human classifiers European Union (ICAR / Interbull) • Fully standardized, ICAR-compliant linear scoring across countries • Shared international genetic pipelines • Excellent interoperability but no automated scoring in practice • Manual measurement remains the bottleneck New Zealand (LIC) • Manual scoring optimized for grazing systems • Robust herd improvement software; high-quality phenotypic records • Automation limited due to variability in field environments Australia (DataGene) • Digital classification tools for structured trait entry • Strong emphasis on functional traits linked to longevity • Standardized but not automated; relies on trained evaluators Across the world, ATC remains mostly manual, even in technologically advanced dairy nations. Automation is emerging but no country has fully achieved it yet. India now has the opportunity to leapfrog straight to AI-powered, standardized ATC, making evaluations more accurate, scalable, and reliable.
2 @SIH Idea submission- Template Research, Dataset Creation, Challenges & Learning SkyNRG Research, Dataset Limitations & Our Field Dataset Creation • Research papers on livestock phenotyping and automated conformation scoring • Keypoint detection frameworks • Global ATC approaches • Existing cow image datasets available publicly Key finding: • We went through multiple public datasets, but none were suitable for ATC. • Most were limited to simple cow detection, and udder-view images were almost nonexistent. • Almost All dataset were of non-Indian Cattles which would deviate from Swadeshi Bharat mission • This made it impossible to train an accurate or production-ready ATC model using existing data. Our Ground-work ATC automation requires precise structural traits, multiple viewpoints, and consistent scaling none of which existed in online datasets. So we committed to gathering real Indian cattle data under farm conditions. For each cow, we collected three essential angles for ATC: • Side View • Rear View • Udder Close-up After filtering out blurred or incomplete photos, we finalized 722 high-quality images. • Farm-wise distribution: 100 cows -> Gir Amrit Dairy Farm, 60 cows -> Desi Dairy Farm, 81 cows –> Sudama Dairy Farm • Breeds : Gir, Red Deoni, Sahiwal, Tharparkar Real Challenges & Learning Our field visits taught us how real ATC conditions behave: • Animals constantly move; stable posture is rare • Background clutter affects computer vision • Lighting varies drastically • Farmers have restricted time during feeding/milking • Safety for both animal and team is essential • These constraints directly shaped our model design and preprocessing pipeline.
5 @SIH Idea submission- Template Annotation Strategy & Trait Extraction Framework SkyNRG Traits Extraction Across all collected images, our team manually annotated: 10000+ anatomical keypoints 3517 trait- specific class labels Trait Extraction Methodology From these annotated key points, we calculated ATC traits using geometric and mathematical relationships, including: 1. Heart Girth & Body Depth (Ellipse Approximation) Using Ramanujan’s Formula for ellipse circumference •Semi-major axis (a) ← chest or body depth •Semi-minor axis (b) ← hip width This allows realistic measurement of body volume & capacity. 2. Angularity Computed through slope differences between anatomical lines. 3. Foot Angle Measured using vector-based angle calculation between hoof and pastern. 4. Rump Angle Using distance between parallel lines from hip bone and pin bone. 5. Rear Leg Conformation •Rear Legs Side View ← angle between hoof and hock vectors •Rear Legs Rear View ← angle between symmetric hock–hoof lines These formulas allow real physical scoring from only a single 2D image, enabled by calibration markers. Rear view Top view Udder Udder Side view Side view
5 @SIH Idea submission- Template Scaling and Measurements SkyNRG Traits Extraction Implementation Key Learning when Automating Scaling: No algorithm can convert image pixels into absolute real-world centimeters without any reference, ever. This is a fundamental limitation of projective geometry. An object twice as large placed twice as far away produces an identical image. Mathematically, there is infinite ambiguity. Our Innovation: Post-it Based Scaling To overcome this mathematically unsolvable problem, we introduced a simple, low-cost, universal calibration method. • We used Post-its of fixed size (2000 purchased for ₹510)as scaling markers during photography. Benefits: • Provides an absolute physical reference • Enables pixel-to-centimeter conversion • Works with any camera (rural phones, low-end devices) • Eliminates need for expensive calibration tools This simple innovation allows the model to calculate real structural traits with confidence. How Scaling Works (Side View) • Marker visible → its pixel dimensions are measured • Actual Post-it size = known constant • A pixel-to-centimeter ratio is computed • Every linear trait (body depth, hip-to-hoof, shoulder angles) becomes measurable in real units Rear View & Udder Scaling Strategy Farm owners often restricted close-up access due to safety and milking schedules. So in these views: • Post-its were not feasible • We captured controlled-distance images. • Then normalized trait distances using the side-view scaling ratio. This maintains consistent scaling across all three viewpoints.
5 @SIH Idea submission- Template Models and Architecture SkyNRG Flow Chart We take five standard views of each animal, classify them, and route them to specialized models. Each model detects anatomical keypoints, converts pixel distances to real-world measurements using a reference object, and computes standardized trait scores. A separate BCS model evaluates body condition. Finally, all outputs: keypoints, measurements, trait scores, and BCS are combined into a single structured JSON for downstream use.
5 @SIH Idea submission- Template SCORING SkyNRG Format for scorecard RESULTS DEMONSTRATION: Scores are assigned using a linear scale of 1 to 9 as recommended by the ICAR system. Each trait is evaluated either through measurement (objective traits) or subjective assessment (visual traits). • 1 = Biological minimum / weakest expression • 5 = Intermediate / average • 9 = Biological maximum / strongest expression The score represents the degree of expression of the trait, not desirability. Measurements are taken using standard tools (measuring tape, goniometer, circumference device), following the NDDB guidelines. Appendix II: Format for scorecard https://www.dairyknowled ge.in/sites/default/files/Gui delines_for_Type_classifica tion_in_Cattle_and_Buffalo _1501.pdf 8
5 @SIH Idea submission- Template RESULTS SkyNRG 8
4 @SIH Idea submission- Template Technical Approach SkyNRG Demo Prototype -> https://ai-animal-type-classification-516520204447.us-west1.run.app/ Screenshot of our Working Prototype Stage Tech Stack ۞ Image Capture Flutter / React Native, Camera APIs, File Upload, Drag-Drop, Bulk Import ۞ Image Quality Check OpenCV, PIL/Pillow (Python backend) ۞ Breed & Animal ID YOLO11m-pose model, FastAPI backend, Ultralytics library ۞ Segmentation & Landmarks YOLO11m-pose for pose estimation, OpenCV, NumPy ۞ Automated Measurements OpenCV, NumPy (calculations based on pose keypoints) ۞ ATC Scoring & Analysis FastAPI backend, NumPy for calculations ۞ GPS & Timestamping Device GPS APIs, EXIF metadata, Secure Embedding ۞ Archival & Storage Browser LocalStorage, Export to Excel (xlsx format) ۞ Continuous Learning Loop Active Learning Pipeline, Human Feedback Queue, Model Retraining Scripts ۞ UI & Accessibility React 19, Tailwind CSS, React Router v6, Framer Motion for animations, React Icons ۞ Offline Support PWA, Service Workers, Local Caching, SQLite/Realm Features Process Flow
4 @SIH Idea submission- Template Aspect Feasibility Technical High – Existing CV models (YOLOv11, HRNet, EfficientNet) and AI frameworks (PyTorch, TensorFlow Lite) can handle segmentation, keypoint detection, and scoring. Mobile + server deployment feasible. Operational Medium-High – Field staff already use mobile apps (BPA), so training needs are low. Offline-first & multi-language features improve adoption. Bulk uploads reduce manual workload. Economic High – Cloud + edge hybrid reduces infra costs. Open-source AI models minimize licensing. Potential savings by reducing manpower and errors in ATC evaluations. Scalability High – API-based design and modular pipeline ensures easy expansion across regions and breeds. Feedback loop enables continuous model improvement. User Adoption High – Mobile-friendly, voice summaries, and multi-language support remove accessibility barriers. Archive + GPS proof builds trust and compliance. Integration High – Unified API layer ensures seamless integration with Bharat Pashudhan App (BPA) and government databases. Feasibility & Viability SkyNRG Risk Assessment • Technical Accuracy Risk - AI misclassifies breed or traits due to poor image quality. • Our Approach - Add image quality checks, human-in-loop correction, and continuous model retraining. • Connectivity Risk - Limited or no internet in rural areas impacts image upload and processing. • Our Approach - Offline-first design with local caching and sync when network is available. • Scalability & Cost Risk - High cloud computation/storage costs with large-scale adoption. • Our Approach - Use hybrid edge + cloud deployment, model compression, and auto-archival to reduce costs. • User Adoption Risk – Field staff/farmers face usability or language barriers. • Our Approach - Provide multi- language support, voice summaries, simple UI, and training for field staff. • Data Security & Privacy Risk - Unauthorized access or misuse of livestock evaluation data. • Our Approach - Enforce encryption, API key authentication, and role-based access. Integration Risk – Delays or incompatibility when integrating with Bharat Pashudhan App (BPA). Our Approach - Build a unified API layer with standardized schema and enable sandbox testing before rollout.
6 @SIH Idea submission- Template RESEARCH AND REFERENCES Core Research ML Models and References https://dahd.nic.in/rashtriya-gokul-missi on Rashtriya Gokul Mission Official Document FAO – Guidelines for Cattle Linear Type Classification https://www.fao.org/3/y4873e/y4873e00 .pdf Microsoft Research Cattle Keypoint Detection Dataset https://www.microsoft.com/en-us/rese arch/project/farmbeats/ Automatic Body Measurement of Cattle Using Deep Learning https://link.springer.com/article/10.100 7/s10453-021-09707-2 Body Condition Scoring https://extension.okstate.edu/fact-she ets/print-publications/afs/body-conditi on-scoring-of-cows-afs-3283-a.pdf Guidelines and Body Conformations Gir Genetic Information https://www.fao.org/4/y4924t/ y4924t07.htm Animal Body Length Measurements https://arccjournals.com/journa l/indian-journal-of-animal-resea rch/B-2498 Animal journal Research https://journalanimalresearch.c om/Journal/abstract/id/MTAzN w== Thank You SkyNRG Bharat Pashudhan App https://pib.gov.in/PressReleasePage.as px?PRID=1970643 Indian Council of Agricultural Research https://www.icar.org/Guidelines/05-Conf ormation-recording-Appendix-1.pdf YOLOv11 https://docs.ultralytics.com