Presentation of work by José B. Camacho with Miguel L. Pardal and Alberto Cunha; presented at INForum 2016 in Lisbon, Portugal

1. Precision Agriculture
with Sensors and Technologies
from the Internet of Things
Authors:
José B. Camacho, Miguel L. Pardal, Alberto R. Cunha

2. The Internet of Things promises
that common real world objects
with limited capabilities can
capture data from the
surrounding environment and
share it with the Internet

3. People Traffic Security Home
We usually assume a urban context, with the needed
energy and communication infrastructures

4. Farm’s management has
evolved and today farmers
use Agricultural
Production Models
Planeamento
Preparação
da Produção
Processo
de Produção
Gestão
dos Produtos
Cadeia
de Valor
Sementeira Manutenção Colheita
Produto
Final

5. Internet of Things Sensors and
Technologies allows farmers to have a
deeper knowledge about their land
and their crops

6. Existing solutions are tied
with large investment and
they are not adapted to
manual labor

7. Agricultural workforce
monitoring system to feed
agricultural production models

8. Monitor workers
locations and activities
using off-the-shelf devices
Smartphone Wearable

9. GPS
Advantages:
• Absolute coordinates system
• Every point is calculated individually,
no error accumulation
Disadvantages:
• High power consumption
• GPS signal is attenuated by tree
canopy
• Average error is high (~5-10m)
2 Olive Orchards Tested

10. Dead Reckoning
X
YZ
Accelerometer
+
Magnetometer
Detect Steps Give Steps a Direction
Inertial Navigation System

11. Dead Reckoning
Advantages:
• Does not depend on technolgies
external to the Smartphone
• Works even close to a tree
Disadvantages:
• Accumulates error over time
• Navigation technology relative to a
point (it is needed to be defined an
initial point)
Navigation alongside
trees

12. • Both technologies showed not to be enough
to correctly locate agricultural workers
• We suggest a combination of both
(Dead Reckoning for navigation + GPS to
correct error)
Location

13. Activity Detection
Classify worker activities during labor day
using Machine Learning algorithms:
BayesNet e MultilayerPerceptron
from Weka library
X
YZ
+ +
Accelerometer Magnetometer Gyroscope

14. Activity Detection
Activities to Monitor:
• Walk Forwards
• Walk Backwards
• Run
• Harvest fruit
• Plow
90% correctly classified agricultural
related activities

15. Future Work
With the results from this study,
create a system to help farmers
with decision making, enabling
them to have a deeper, richer
knowledge about their farms

16. Questions?
{jose.camacho, miguel.pardal, alberto.cunha}
@tecnico.ulisboa.pt

18. Dead Reckoning

19. Olival Intensivo

20. Olival Tradicional