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1
Introduction
2
Data Science Africa
3
Custom board
4
Local solutions
5
Q42 Poacher Detection
6
Cow Fitbit
7
Elephant tracking
8
Animal tracking
9
Why use tinyML
10
Challenges
11
Data quality
12
Data logger
13
Open camera trap
14
Challenges in Africa
15
Conclusion
16
Get involved
17
Questions
18
The future
Description:
Explore how embedded machine learning can revolutionize agriculture and conservation in Africa through this insightful talk. Discover the potential of sensors to understand the world autonomously, from detecting cow health to monitoring tomato growth and elephant proximity. Delve into Jan Jongboom's experiences in East Africa, examining problems encountered and innovative ML solutions. Learn about the importance of power consumption in these applications and gain insights into running after cows on a Tanzanian dairy farm. Examine topics such as Data Science Africa, custom boards, local solutions, poacher detection, animal tracking, and the challenges of implementing tinyML in African contexts. Gain valuable knowledge on data quality, open camera traps, and future possibilities in this field.

Chasing Cows - Making Africa Smarter With Embedded ML

tinyML
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