BIO-AI Research
Phase 3
/// BIO_AGRO_RESEARCH
Project Endemica
Precision Agriculture for Endemic Flora via Computer Vision.
94.2% ACCURACY
247 SENSORS
12 SPECIES
/// METHODOLOGY
01 COMPLETED
Big Data Collection
IoT sensors deployed across endemic habitats. 50M+ environmental data points collected over 12 months.
247 sensors 50M+ data points 12 months
02 ACTIVE
Low-Frequency Tracking
LoRaWAN energy optimization for long-range, low-power monitoring. Mesh network covering 500 hectares.
500 hectares 15km range 40% energy saved
03 PLANNED
Autonomous Greenhouse
Full AI climate control with zero human intervention. Closed-loop systems for humidity, temperature, nutrition.
30+ day autonomy 95% water recycling 300% yield
/// Data Pipeline Architecture
IoT Sensors 247 nodes
→
Edge Gateway RPi 5 + TPU
→
Vision AI ViT-B/16
→
Action Actuators
500Hz Sample Rate
<100ms Inference
15km LoRa Range
99.9% Uptime
/// TECHNICAL SPECIFICATIONS
COMPUTER VISION
Vision Transformers (ViT-B/16)
TensorFlow Lite
Coral Edge TPU
<100ms inference
CONNECTIVITY
LoRaWAN Class C
15km range
50kbps uplink
Solar + 72hr battery
EDGE COMPUTE
Raspberry Pi 5 (8GB)
Python 3.11 + FastAPI
NVMe 256GB
S3 sync
CLOUD & DATA
AWS IoT Core
TimescaleDB
Grafana
SageMaker ML Pipeline
ViT-B/16 Vision Model
TF Lite Runtime
LoRaWAN Protocol
RPi 5 Edge Device
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Interested in Collaboration?
We partner with research institutions, agricultural organizations, and conservation groups.
Contact Research TeamDoc: C3T-RD-ENM-2024 | Rev: 3.2.1 | Classification: Public