Apollo AgriGuard
Transforming precision agriculture through integrated robotics, AI/ML, and cloud platforms for continuous farm monitoring and automated interventions.
Executive Vision
What Apollo Is
An extensible system-of-systems combining autonomous data collection, real-time telemetry, AI-driven detection and decisioning, operator dashboards, and deployment automation. Designed for small-to-medium agricultural sites with paths to scale for commercial farms and enterprise customers.
Core Mission
Reduce crop management costs and crop loss by enabling continuous sensing and automated, targeted action. Convert raw field data into actionable agronomic intelligence for irrigation, pest/disease mitigation, fertilizer optimization, and yield forecasting.
Value Proposition
30%
Resource Reduction
Projected reduction in agricultural resource wastage through precision targeting and continuous monitoring.
40%
Prediction Accuracy
Improvement in crop-yield prediction accuracy using AI-driven analytics and digital twin simulations.
25%
Cost Savings
Reduction in operational farming costs through automated interventions and optimized resource allocation.
Apollo accelerates farm R&D using realistic simulation, digital twins, and reproducible model training, specifically designed for the 2-acre farmer on the Deccan Plateau.
System Architecture Overview
Device Layer
Rover/lander platforms with edge compute, GNSS, IMU, camera arrays, environmental sensors, and optional RTK positioning.
Connectivity
LTE/5G or Wi-Fi backhaul with store-and-forward fallback. Secure TLS transport with MQTT/ROS2 DDS messaging.
Edge Compute
Containerized inference for crop health, obstacle detection, and navigation with local buffering and encryption.
Cloud Backend
Time-series database, batch ML training, data labeling pipelines, and REST/WebSocket APIs for dashboards.
The Four Pillars of Apollo
Cognitive Shield
Advanced sensing and prediction capabilities that monitor field conditions continuously, detecting anomalies and forecasting issues before they impact crops.
Kinetic Hand
Autonomous robotics and precision action systems that execute targeted interventions based on real-time data and AI recommendations.
Controlled Environment
Vertical farming integration and climate-controlled systems that decouple productivity from weather volatility.
Digital Spine
Backend infrastructure and RAG systems that process, store, and analyze agricultural data for continuous learning and optimization.
Hardware Stack
Core Components
  • Compute: NVIDIA Jetson (Nano/Xavier) or industrial SBC for GPU inferencing
  • Sensors: RGB cameras (stereo), multispectral imagers, thermal cameras, ToF/LiDAR, soil moisture probes, environmental sensors, IMU, RTK-GNSS
  • Actuation: DC motor drivers, encoders, servo controls for tool actuation
  • Power: Modular battery packs with fuel gauge, solar-assisted options for long deployments
Prototype Budget
Single field prototype: $3k–$18k depending on sensor choices and compute specifications. Includes chassis, motors, sensors, edge compute, power systems, and enclosures.
Software Stack
1
Languages
Python (primary), C++ for ROS2 nodes, JavaScript/TypeScript for frontend dashboards
2
Middleware
ROS2 for robotics messaging, MQTT/DDS for telemetry bridging
3
Backend
Flask/FastAPI, time-series DB, object storage, Postgres for relational data
4
Deployment
Docker containers, docker-compose orchestration, Kubernetes for scale
AI & Machine Learning Strategy
01
Crop Health Scoring
Disease and pest detection from RGB and multispectral imagery using lightweight CNNs and MobileNet-class backbones for edge inference.
02
Anomaly Detection
Time-series telemetry analysis for sensor drift, environmental anomalies, and equipment faults with continuous monitoring.
03
Yield Prediction
Growth-stage estimation and harvest forecasting using transformer ensembles for cloud analytics.
04
Autonomous Navigation
SLAM and model-assisted control with reinforcement learning agents trained in simulation for path planning and action selection.
Data Pipeline & Training
Bootstrap datasets using simulation and synthetic augmentation with domain randomization, then fine-tune on field-collected labeled data. Pipeline includes data ingestion, annotation, versioned datasets, model training, validation, and deployment to edge and cloud.
Robotics & Autonomy
Navigation & Perception
  • RTK/GNSS-assisted waypoint navigation with local obstacle avoidance
  • Object detection, field boundary detection, row detection for row crops
  • Plant-level segmentation for targeted action
  • Mission scripts for survey patterns, spot checks, targeted interventions
Safety Systems
  • Physical E-stops and geo-fencing
  • Fail-safe behaviors on communication loss
  • Modular actuation features for regulatory compliance
Completed Deliverables
1
Core Infrastructure
Repository structure, modules, baseline documentation, and deployment artifacts established.
2
ROS2 Integration
ROS2 bridge and basic robotics integration completed in act/ros2_bridge/.
3
Simulation Stack
Field simulation, digital twin components, and Wokwi lander simulation operational.
4
Dashboard Systems
Enterprise dashboard app with Streamlit, Docker deployment, and launch scripts ready.
5
Analytics Tools
Lander analyzer and monitor scripts with field data files and example telemetry.
6
DevOps Foundation
Docker, docker-compose, Nginx config, and CI-friendly packaging complete.
Current Development Focus
ROS2 Enhancement
Improving bridge robustness and integration tests between hardware simulation and real device controllers.
Dashboard Evolution
Enhancing UI/UX and metric pipelines with improved KPI generation and real-time updates.
Telemetry Infrastructure
Building robust storage and streaming layers with time-series ingestion, compression, and archival.
ML Model Training
Developing prototype models for anomaly detection and crop-health scoring using simulation-augmented datasets.
Short-Term Roadmap
1
Field Testing
Deploy rover/lander prototypes with integrated sensors: camera, multispectral, IMU, GNSS, environmental sensors.
2
Data Pipelines
Implement end-to-end flows: device → edge compute → secure cloud ingestion → model inference → dashboarding + alerts.
3
Simulation Expansion
Expand scenarios and synthetic data generation for better model generalization and validation.
4
Pilot Program
Launch GTM pilot with 2–3 partner farms, refine pricing and support model based on feedback.
Enterprise Dashboard
The Apollo Enterprise Dashboard provides real-time monitoring, historical analytics, and actionable insights. Built with Streamlit and Flask, it features market intelligence, field health mapping, sensor telemetry visualization, and automated alerting systems for farm managers and agronomists.
Digital Twin & Simulation
Capabilities
  • Realistic field environment simulation
  • Offline validation and testing
  • Reinforcement learning training
  • What-if scenario analysis
  • State snapshots and replay logs
Digital twin representations enable debugging, pilot validation, and research-grade agronomic insights without field deployment risks.
Lander Analysis System
Temperature
Range: 15-35°C. Monitors ambient conditions for optimal rover deployment and crop health assessment.
Soil Moisture
Range: 20-80%. Critical parameter for irrigation decisions and deployment safety assessment.
Wind Speed
Max: 8.0 m/s. Ensures safe operating conditions for autonomous navigation and sensor accuracy.
Humidity
Range: 30-90%. Monitors atmospheric moisture for disease prediction and equipment protection.
The lander analyzer determines rover operational readiness by evaluating sensor data against defined thresholds, categorizing plots from OPTIMAL to CRITICAL status.
Deployment Status Categories
EXCELLENT
All parameters within optimal range. Rover deployment highly recommended for maximum efficiency.
GOOD
Minor deviations detected. Safe for deployment with standard monitoring protocols.
FAIR
Multiple parameter warnings. Deployment possible with enhanced caution and monitoring.
POOR
Significant violations detected. Deployment risky, recommend waiting for improved conditions.
CRITICAL
Severe environmental issues. Deployment not recommended until conditions stabilize.
Business Model
Hardware Sales & Leasing
Sell rover/lander kits or offer flexible unit-month lease plans for farms of all sizes.
SaaS Analytics
Subscription-based dashboard access, alerts, model insights, and historical reporting services.
Managed Services
On-site setup, data labeling, model customization, and agronomy consulting for enterprise clients.
Funding & Budget Plan
12-18 Month Pilot Phase
  • R&D & Engineering (4-6 FTEs): $400k–$800k
  • Hardware Prototyping (3 units + spares): $30k–$80k
  • Cloud & Data Infrastructure: $20k–$80k
  • Field Testing & Logistics: $20k–$50k
  • Sales & Pilot Onboarding: $30k–$80k
Fundraising Targets
Seed Round (Pilot-Ready): $600k–$1.2M for initial deployment and validation.
Series A (Scale & Manufacturing): $2M–$6M for commercial production, sales expansion, and market penetration.
Key KPIs
Pilot conversion rate, ARR from SaaS, unit economics, customer acquisition cost (CAC), time-to-value for farm operations.
Partnership Structure
MindForgeAI
Model research, ML infrastructure, software engineering, simulation/digital twin development, and backend services.
GreenWork
Hardware engineering, field operations, agronomy expertise, pilot management, and domain validation.
Chatake Innoworks Pvt. Ltd.
Technology Partner
Chatake Innoworks is the primary technology partner for Apollo, operating at the intersection of AI, Robotics, Agriculture, Industry 4.0, and Sustainable Systems.
Location: Nehru Industrial Estate, Solapur, Maharashtra, India (PIN: 413001)
LinkedIn: Chatake Innoworks Pvt. Ltd.
Core Capabilities
Research & Development
ML model research, digital twin and simulation, systems engineering for cyber-physical systems.
Product Development
Prototyping robotic landers/rovers, sensor integration, embedded systems using ROS2.
Cloud & Backend
Scalable ingestion, time-series analytics, model-training pipelines, dashboarding solutions.
Pilots & Field Ops
On-site integration, agronomy consulting, data labeling and model validation in real environments.
Project Team
Aarthika Anil Birajdar
Group Leader
Project coordination and strategic direction
Mr. Akash Shivadas Chatake
Project Guide
Shri Siddheshwar Women's Polytechnic
Vaishnavi Dhuttarge
IoT & Sensors
Hardware integration and sensor systems
Anuja Gurav
Robotics/ROS
Autonomous systems and navigation
Siddheshari Degaonkar
Frontend Development
Dashboard and user interface design
Sharvari Garad
Research & Database
Data architecture and analytics
Ojausvi Bhave
Database Management
Data operations and optimization
Risk Management
1
Hardware Reliability
Risk: Field condition challenges
Mitigation: Robust enclosures, sensor redundancy, thorough environmental testing
2
Simulation-Field Gap
Risk: Model performance differences
Mitigation: Domain adaptation, continual learning, active labeling programs
3
Connectivity Constraints
Risk: Intermittent network access
Mitigation: Store-and-forward, edge-first inference, offline capabilities
4
Regulatory Compliance
Risk: Actuation restrictions
Mitigation: Modular design with enable/disable features for different regions
Security & Compliance
Security Measures
  • Transport Encryption: TLS for all data transmission
  • Device Authentication: Mutual TLS or token-based authentication
  • Access Control: Role-based permissions for dashboard and APIs
  • Data Privacy: Consent-driven capture, anonymization of geolocated personal data
Compliance Framework
  • Secure storage with fine-grained access control
  • Data residency compliance for pilot regions
  • Ethics-first approach to data collection
  • Regular security audits and monitoring
12-24 Month Roadmap
0-6 Months
Finalize ROS2 bridge robustness, complete telemetry schema, run closed-field tests, produce 2-3 pilot units, start first farm pilots.
6-12 Months
Mature ML models for crop health and anomaly detection, integrate field feedback loop, onboard first commercial customers, improve UI/UX.
12-24 Months
Scale manufacturing and fulfillment, add enterprise features (fleet management, advanced integrations), expand to new crop types and geographies.
Success Metrics
30%
Scouting Time
Reduction in manual field inspection requirements
45%
Early Detection
Improvement in disease/pest identification rates
85%
Customer NPS
Pilot customer satisfaction and recommendation score
40%
ARR Growth
Annual recurring revenue increase from SaaS subscriptions
Technology Stack Summary
Edge Layer
ROS2, Python, C++, Docker containers, MQTT/DDS messaging
Cloud Layer
Flask/FastAPI, TimescaleDB, PostgreSQL, S3-compatible storage, Redis cache
ML Pipeline
PyTorch, TensorFlow, MobileNet, CNNs, Transformers, RL agents
DevOps
Docker, docker-compose, Kubernetes, Nginx, CI/CD pipelines
Frontend
Streamlit, JavaScript/TypeScript, Plotly, modern web frameworks
Project Context & Recognition
DIPEX 2026 Showcase
Apollo is being showcased at DIPEX 2026, demonstrating cutting-edge integration of robotics, AI, and precision agriculture. Developed in collaboration with Shri Siddheshwar Women's Polytechnic.
Resources & Links
Join the Agricultural Revolution
Apollo represents the future of precision agriculture—where autonomous systems, artificial intelligence, and agronomic expertise converge to create sustainable, profitable farming operations. We're transforming how the world grows food, one field at a time.
For Farmers
Reduce costs, increase yields, and gain unprecedented insights into your fields with continuous monitoring and automated interventions.
For Investors
Join us in scaling a proven technology with clear unit economics, strong pilot traction, and massive market potential.
For Partners
Collaborate on cutting-edge agtech solutions that combine hardware, software, and domain expertise for real-world impact.
© 2026 Chatake Innoworks Pvt. Ltd. | Apollo AgriGuard Project