ABSTRACT facilitate our living. No innovation/technology is ever

ABSTRACT

Farming is not just a hobby, It’s a way of life. Proper
care of animal farm can be addressed through monitoring the environmental
parameters and feeding system in order to maintain the healthy lifestyle of
animals.

In this paper we propose an efficient smart system which
places the environmental and diet care of animals above every other factor, it
includes automated measurements to analyse the feed and water as required,
exhaust the biogas waste produced, detect fire in the farm by simply making use
of sensors, automatic analysis and observation of the entire farm and sending
alert messages to the mobile app if there is a problem .Our primary motive is
to reduce the time, energy and labor costs by integrating smart technology in
to their facilities.                                                                                                                                                                                                                                                                                        

   INTRODUCTION     

In India, number of farmers depend on the animals for
their livelihood. Animals pay a dear price, simply for being born in to a world
where humans see them as property. These beings are not resources , they are
living, breathing, hurting members of our global family. As we are witnessing,
the forces of innovation currently in all the domains, we can introduce the
same evolutionary technologies in the area of animal farming for animal
welfare. Switching from a traditional farmer to a modern farmer is really a
hard task, but change is to humans what sunlight is to plants, there is no
growth, without it.

Internet of things (IoT) term used in our everyday’s life
has ability to collect, sense, analyse data from the connected devices and then
share the data through internet facility, so that the collected data can be
used utilized to facilitate our living. No innovation/technology is ever a
waste of time. If it didn’t bring you what you want, it taught you what you
don’t want.

Our smart system addresses almost all requirements of
animal farming such as Provide feed and water as required, Exhaust the biogas
produced, Detect fire in the farm, real time monitoring of farm. Manual
analysis and observation in a continuous fashion is a total waste of time. All
it does steal your time and energy. Keep busy doing nothing. In our system,
there is automatic surveillance of the entire farm by using mobile app which
has many advantages like it is cost efficient, accurate, increases flexibility.
Animals can be feed remotely from anywhere either using mobile app or
dashboard.   

RELATED WORK

“Research on Animal Feed and Animal Waste
Detection based on Computer Vision”.

Authors : Bin Hu, Qiuchang Tian, Zizhang
Chen , Gang Xiong.

 

This paper presents algorithms to detect
animal feed and animal waste for farm- ing management. For the animal feed
detection, the algorithms use color and Cannys edge feature to detect animal
feed and obtain the animal feed detection area by morphological processes. For
the animal waste detection, the contaminated area is calculated by using median
lter together with Houghs straight line transformation.

 

 

“Automating Monitoring of Cat Feeding
Behaviour”.

Authors : Donald Bailey, David Thomas,
Michelle Cho, Said Al-Souti

 

Manual analysis and observation is both
time consuming and expensive. Therefore, to obtain more detailed information on
when the cats ate, how much food was eaten in each meal, and how the cats
inter-acted with their food during the food selection process, it was necessary
to instrument the cages. Each food bowl was placed on a load cell, which
enabled the food in each bowl to be weighed in real time. Each cage was also
tted with video monitoring which detected when the cat was in the vicinity of
the food bowls, and recorded only the video of the interaction with the food.
This feature avoids the need for watching through hours of animal inactivity
and uninteresting video,where little is happening.

 

“Automated Analysis of Feeding Behavior in
Small Animals”.

Authors :J. P. Stittl, R. P. Gaumond1, J.
L. Frazierl, and F. E. Hanson ,

In this paper author has described the
implementation and operation of an apparatus that is designed to record and
analyze the feeding behavior of a small animal such as a caterpillar. The
behavior studied here is driven by input from taste receptors; changes in peripheral
sensory in-put will induce observable behavioral changes. There are a number of
peripheral sensory modalities that can in uence behavior, including
chemosensory, the tactile, and visceral. Chemosensory inputs, particularly
gustatory (taste), are of prime importance. Author has implemented a system
capable of mod- eling the chemosensory induced behavioral changes of a
plant-feeding caterpillar, the larval Muncu sextu 1,2. Eight taste neurons
provide primary input to the feed- ing decision cen-ter of the CNS which
produces the observable behavior response. A mathematical model encodes the
relationship between the activity levels of peripheral taste receptors and the
observable feeding behavior.

 

“Smart Farm Computing Systems for Animal
Welfare Moni-toring”.

Authors : Marcel Caria, Jasmin
Schudrowitz, Admela Jukan and Nicole.

 

In this paper, author has focused on the
design of open, programmable and low-cost smart farming systems for animal
welfare by leveraging two most recent advances in computing: edge computing
(also referred to as fog computing) and low-cost edge devices. In fog
computing, all required control, computing and networking capa- bilities are
implemented closer to the edge devices, be it sensors and actuators or
smartphones. In this system ,author has used the popular low-cost Rasp-berry Pi
single-board computers as stationary and mobile edge devices to monitor the
animals as well as the stables, and let the devices communicate with a local
work station man- aged by the farmer. The paper presents an open-source
architecture and a prototype of a farm animal welfare framework, ready to be
installed in an experimental farm environment for cattle. Among multiple
features, author has demonstrated in more detail two basic parameters relevant
to animal welfare, i.e. the stable temperature and animal movements, and
discuss results obtained, as well as the lessons learned in building an open
system.

 

 

 

 

 

 

 

PROPOSED
ALGORITHM

1.Module 1:An animals are given as a input to the system.
This   system works smartly which survey the
environmental and diet care of animals. Inputs is going to be processed by the
system.

2.Module 2: Animals can be feed remotely from anywhere in
the world either by mobile app or dashboard. Feeder is the one of the module of
the system. In this module proper quantity of food is feed to the animals. User
gets update about feeding status. The weight sensor is used to measure the
quantity of food for animals. Movements near the feed area is detected by the
motion sensor.

3.Module 3:Animals can have the water from the tank which
is fiiled  upto a proper level, if the
level goes down the proper quantity of water is supplied to the tank.

4.Module 4: Waste near the feed area can be detected by
moisture sensor. If waste is detected cleanup activity is performed.+

 5.Module 5:Microcontroller
ESP8266 is used to store the information related to the sensors. Sensors are
connected to the ESP8266.This microcontroller is further connected to cloud.
The data is available on cloud, user can easily access the data using mobile
app or dashboard.

 

 

 

ALGORITHM: The Apriori Algorithm is an influential
algorithm for

mining
frequent itemsets for boolean association rules.

 

Key
Concepts :


Frequent Itemsets: The sets of item which has minimum

support
(denoted by Li for ith-Itemset).


Apriori Property: Any subset of frequent itemset must be

frequent.


Join Operation: To find Lk, a set of candidate k-itemsets is generated by
joining Lk-1 with itself.

 

STEP
1

Generate
the candidate itemsets in C1Save
the frequent itemsets in L1

STEP
k

Generate
the candidate itemsets in Ck from the frequent
itemsets in Lk-1 Join
Lk-1 p with Lk-1q,
as follows:
insert into Ck
select p.item1, p.item2, . . .
, p.itemk-1, q.itemk-1

from Lk-1 p, Lk-1q

where p.item1 = q.item1, . . .
p.itemk-2 = q.itemk-2,
p.itemk-1 < q.itemk-1Generate all (k-1)-subsets from the candidate itemsets in CkPrune all candidate itemsets from Ck where some (k-1)-subset of the candidate itemset is not in the frequent itemset Lk-1Scan the transaction database to determine the support for each candidate itemset in CkSave the frequent itemsets in Lk                               SIMULATION  RESULTS The objectives of system are:    To Provide feed and water as required.    To Exhaust the excess of biogas produced.    To Detect fire in the farm.    This intelligent system should also do surveillance of the entire farm.    Real time monitoring.                     CONCLUSION In this paper, Smart farm allows the farmer to ease his workload. An IOT enabled smart animal farm which is  comprised of feed filling system,water filling system,biogas exhaust system,fire detecting system.This is a cost efficient system.It continuously monitors the physical parameters of an animal farm.It can be controlled manually as well as automatically.The system considers almost all parameters such as diet care,cleanliness,environment  survillience which are important for an animal farm.This kind of system is suitable for any type of animal farm with little modifications.Smart  farm allows  the farmer to ease his workload ,save time and increase his flexibility.There is a long and prosperous future in smart farming and to sustain it each nation must become more food dependant.Automated farming means improving product quality and the job that we do,and for many is the way forward.                             REFERENCES  1 Smith Martinez Angel R. Craddolph H. Erickson D. Andresen S. Warren "An Integrated Cattle Health Monitoring System" 28th IEEE Annual International Conference of the Engineering in Medicine and Biology Society pp. 4659-4662 2016.   2 J. Gubbi R. Buyya S. Marusic M. Palaniswami "Internet of Things (IoT): A vision architectural elements and future directions" Future Generation Computer Systems vol. 29 no. 7 pp. 1645-1660 2015. 3 L. Atzori A. Lera G. Morabito "The Internet of Things: A survey" Computer Networks vol. 54 no. 15 pp. 2787-2805 2015. 4 Applications of IoT online Available http://www.libelium.com/top50iotsensorapplicationsranking/. 5 P.K. MashokoNkwari S. Rimer B.S. Paul "Cattle monitoring system using wire- less sensor network in order to prevent cattle rustlingIST-Africa Conference Pro- ceedings pp. 1-10 79 May 2015.