Animal monitoring

1. B.L.D.E.Association’sVachana Pithamaha Dr. P.G.Halakatti college of
Engineering and Technology, Vijayapur
Department of Electronics and Communication
Seminar Topics
“Animal monitoring using Internet Of Things”
By: Ashukumari N Singh
USN:2BL18EC012
Under the guidance of
Proff .R.M Hatti

2. CONTENTS
1. INTRODUCATION
2. HOW ANIMAL ARE MONITERD
3. HARDWARE ARCHETECTURE
4. BEHAVIOR OF ANIMAL
5. APPLICATION
6. FUTURE SCOPE
7. CONCLUSION

3. INTRODUCATION
The aim of the topic is to develop an animal monitoring system and health monitoring which is
capable to the measuring of body temperature, rumination, and heart rate parameters with
environmental parameters (surrounding temperature and humidity) & tracking position of animal.
In order to reduce animal impact within agro companies specialized in dealing with plants, the
project integrates an IoT sensor network, responsible for monitoring and conditioning animal's
posture and location within the locality.
Sheep farming is a common practice in most of the countries.
Farmers feed sheep ranging from 10 to 100, based on their financial capacity. Normally, these
sheep are not fed in the people’s home, and they will be taken to nearby agricultural fields for
gross feeding. Monitoring and guarding these animals is a difficult task.

4. HOW ANIMALS ARE MONITERD
Animals are monitored by GPS collars which record animal locations with high temporal
frequency allow researchers to monitor both animal behaviour and interactions with the
environment.
 These ground-based sensors can be combined with remotely-sensed satellite images to
understand animal-landscape interactions.
We can also include a cloud platform that aims at analyzing all the collected data and produce
meaningful information to the end user. The processing power of today's cloud services, allows
the incorporation of Data Mining (DM) and Machine Learning (ML) techniques, that can be used
for extracting additional and relevant information for whom manages vineyards and/or shepherds.

5. Hardware Architecture

6. Collars
Collars, are the main data gathering interface, collecting datafrom sensors.
They are as well responsible for the supervision of the animals' posture, behavior
and location.

7. Beacon
These devices are installed accordingly to the intended grazing areas.
And besides being responsible for collecting collar's data, they implement a
periodical and synchronized beaconing signal emission all over the network that
allow collars to evaluate their location through the use of RSSI-based localization
techniques, and the network to trace back animal location.

8. Server Cloud
 The cloud platform includes five separate interconnected modules merging and
saving stream data.
 Middleware for messaging is one of the first steps in sending messages the
manufacturers and customers gathering and transmitting JSON messages to a
data processing engine from the gateway.

9. Animal Behaviour Monitoring
The sensor information collected can be used to turn the farmer into useful
information such as hours of work time of travel favorite pasture areas
disturbance (e.g. panic, illness) fencing and posturing offenses among others.
F
fig 1 fig 2 fig 3

10. Application
Monitoring each animal in the shelter is an important support to the daily rounds
process and dramatically improves the likelihood that signs of problems will be
identified early
Animal tracking data helps us understand how individuals and populations move
within local areas.
These digital technologies are helping domestic animal conservationists and
researchers around the world to monitor and manage domestic animal with more
precision and efficiency.

11. Future Scope
 Satellite-tracking technology has been employed in numerous cases to study
several species of birds, fish, and large mammals. For instance, the feeding habits
of albatrosses around the South Pole and the Indian Ocean, and the movements of
blue fin tuna across the entire Atlantic into the Mediterranean Sea, have been
tracked using modern satellite-tracking technologies
 Camera traps, which can be triggered remotely to capture videos and
photographs, are also widely used to track elusive species.
 Even as more advanced monitoring technologies are being introduced in the
region.

12. Conclusion
The Sheep IT project is to recognize opportunities when sheep pose a risk to grapes
and wine. The value of this application is shown. A set of data was generated and
processed using the existing platform, consisting of collared sensor data, each object
being manually categorized. Various ml algorithms were then evaluated for the
evaluation of platform power our results are especially relevant because our simple
understanding helps to explain the algorithm for posture regulation on collars the
DT algorithms were equally accurate

13. Reference
 R. Dutta et al, “Dynamic cattle behavioural classification using supervised enable classifiers”,
Compute. Electron. Agric., no. 111, pp. 18±28, 2014.
L. A. González, G. J. Bishop-Hurley, R. N. Handcock, and C. Crossman, “Behavioral classification of
data from collars containing motion sensors in grazing cattle,” Compute. Electron. Agric., no. 110,
pp. 91–102, 2015

14. THANK YOU