Gallery

Coffee Cherry Ripeness Detection: Empowering Farmers with AI

Our innovative project harnesses the power of artificial intelligence to revolutionize coffee harvesting. We’ve developed a TensorFlow Lite (TFLite) model that accurately detects the ripeness of coffee cherries in real-time, directly in the field.
How It Works

Image Classification: The model analyzes images of coffee cherries, categorizing them into three stages of ripeness:
- Green (unripe)
- Ripe
- Overripe
Edge Device Deployment: Converted to TFLite, the model runs efficiently on devices like Raspberry Pi or smartphones.
Real-Time Detection: Farmers can instantly assess cherry ripeness using their mobile devices.

Benefits for Coffee Farmers

Optimal Harvest Timing: Ensures cherries are picked at peak ripeness.
Quality Improvement: Leads to better-tasting, higher-quality coffee.
Efficiency: Streamlines the harvesting process, saving time and labor.
Consistency: Reduces variability in harvest quality.

This technology empowers coffee farmers to make data-driven decisions, ultimately enhancing the quality of their harvest and potentially increasing their income. By bridging the gap between artificial intelligence and agriculture, we’re cultivating a smarter, more sustainable future for coffee production.

Revolutionizing Goat Age Detection: A Cutting-Edge Veterinary Science Project

We’re excited to showcase an innovative project that combines veterinary science with advanced machine learning techniques. This groundbreaking work, led by MVSc student Haleema H. from Government Veterinary College, Mannuthy, focuses on goat dentition and leverages the power of YOLOv8 for accurate age detection.

Project Highlights:

Advanced Age Detection: Developed a model using YOLOv8 to accurately determine goat age based on dentition, a crucial aspect of veterinary science.
Mobile Implementation: Successfully converted the model to TFLite and implemented it in a mobile application using Android Studio, ensuring efficient on-device performance.
Award-Winning Research: Haleema H. presented this project at the Kerala Veterinary Science Congress, securing the First Prize for her outstanding work.

Key Achievements:

Created a precise goat age detection model using state-of-the-art YOLOv8 technology.
Developed a functional mobile application that provides accurate results in real-time.
Demonstrated the practical application of machine learning in veterinary science.

Collaborative Effort:

This project’s success is a testament to the power of collaboration in scientific research. Special thanks to:

Prof. Prasad and Dr. Pramod S. of Avian Research Station, Thiruvizhamkunnu, for their invaluable guidance and expertise.
The supportive team at Government Veterinary College, Mannuthy.

Looking Ahead

This achievement marks a significant step forward in veterinary science and showcases the potential of integrating advanced technologies into animal health practices. We’re excited about the future possibilities and look forward to further innovations in this field.

Congratulations to Haleema H. for her exceptional work and dedication to advancing veterinary science through technology!

Advancing Wildlife Conservation Through Drone Technology
Training Session with Kerala Forest Department’s Rapid Rescue Team

We recently had the privilege of conducting specialized training for the Rapid Rescue Team at Kerala’s Aralam Wildlife Division, Kannur. This initiative focused on deploying the DJI Mavic 3 Thermal Drone – a game-changing tool for wildlife detection and rescue operations in challenging forest terrains.

Key Training Components

Precision Mission Planning
- Mapping & Waypoint Navigation: Enabled teams to design systematic flight paths covering 15+ km² of dense forest in a single 45-minute session1 4
- Linear Tracking Protocols: Implemented specialized flight patterns to monitor elephant migration corridors and identify herd movements in real-time
Advanced Imaging Techniques
- Thermal Analysis Mastery: Leveraged the drone’s 640×512 px radiometric thermal sensor to detect animals through vegetation (detection range: 40-200m)8
- Night Operations: Demonstrated thermal imaging’s superiority in nocturnal wildlife monitoring, where ambient heat signatures stand out clearly6 8
Data-Driven Conservation Tools
- Photogrammetry Workflows: Created centimeter-accurate 3D terrain models for habitat analysis using the 20MP visual camera5
- Multi-Sensor Integration: Combined thermal data with RGB imagery through DJI Pilot 2 software for comprehensive situational analysis1 4

Technology in Action

The Mavic 3 Thermal’s capabilities proved particularly effective for:

Hidden Animal Detection: Locating species concealed in dense canopy cover (successfully demonstrated in koala conservation projects6)
Risk-Free Monitoring: Conducting observations from 120m altitude to avoid disturbing sensitive wildlife6
Rapid Response Coordination: Streaming real-time thermal feeds to ground teams via DJI FlightHub 2 platform1 4

Operational Impact

This training aligns with global best practices in conservation technology, mirroring successful deployments:

63% faster response times in Australian wildfire rescues using similar thermal protocols6
40% improvement in detection accuracy compared to traditional spotlight surveys8

Why This Matters
As demonstrated in polar bear den monitoring6 and Kenyan anti-poaching operations, thermal drones are revolutionizing wildlife management. Our session equipped the Kerala team with:

Enhanced night operation capabilities
Data collection methods for long-term habitat studies
Immediate implementation protocols validated by IUCN conservation guidelines

“The integration of thermal imaging with AI-powered analysis represents a paradigm shift in how we protect endangered species.” – Wildlife Technology Institute Report 2024

This collaboration underscores our commitment to bridging cutting-edge technology with ecological preservation. We’re eager to support more agencies in adopting these vital 21st-century conservation tools.

Equipment Specs Highlight:
DJI Mavic 3 Thermal | 45-min flight time | 56x hybrid zoom | AES-256 encrypted data | -20°C to 50°C operational range1 5