Smart Farm
Agriculture is the primary occupation in our country for ages. But now due to migration of people from rural to urban there is hindrance in agriculture. So to overcome this problem we go for smart threshing techniques using IoT. Smart Farming is a recent advancement of IOT in
2025-06-28 16:35:21 - Adil Khan
Smart Farm
Project Area of Specialization Internet of ThingsProject SummaryAgriculture is the primary occupation in our country for ages. But now due to migration of people
from rural to urban there is hindrance in agriculture. So to overcome this problem we go for
smart threshing techniques using IoT. Smart Farming is a
recent advancement of IOT in agricultural domain for increasing the quantity and quality of crops.
This project includes various features
like GPS based remote controlled monitoring, moisture & temperature sensing, leaf wetness,
detection of disease in crops and proper irrigation
facilities. It makes use of wireless sensor networks for noting the soil properties and
environmental factors including humidity, temperature, and climate quality continuously. The system will detect the
diseases of crops as well. Various sensor nodes
are deployed at variegated locations in the farm. Controlling these parameters are through internet services and the
operations are performed by interfacing sensors, Wi-Fi/2G/3G module, camera with a quadcopter. The system was found
to be comfortable for farmers to use as they could effectively control the farm anywhere at any
time, resulting in cost reduction, asset saving, and productive management in smart farming.
This concept will be created as a product and will be given to the
farmer’s welfare.
To modernize and stabilize the yield yields of smallholder farmers through the
implementation of sustainable irrigation systems.To promote water management practices that
optimizes the volume and timing of water distribution. To generate positive economic
consequences for farmers and their families. Minimize year to year yield fluctuations, leading to
higher and increasingly stable sublet income. This system consists of a
water pump withal with an will-less water spritz tenancy using a moisture sensor. It is the proposed
solution for the present energy slipperiness for all the farmers virtually the globe. This system conserves electricity by
reducing the usage of grid power and conserves water by reducing water losses.
While conventional methods include pumping of water from sink well into a well and from this
well onto field using flipside pump, our system uses only a single stage energy consumption
wherein the water is pumped into a ground level tank from which a simple valve mechanism
controls the spritz of water into the field. This saves substantial value of energy and efficient use
of renewable energy. A valve is controlled using intelligent algorithm in which it regulates the
flow of water into the field depending upon the moisture requirement of the land. In this system
we use a soil moisture sensor that detects the value of moisture present in the soil and depending
upon the requirement of level of moisture content required for the yield the water spritz is regulated
thus, conserving the water by lamister over flooding of crops.
Reducing the waste which is due to diseases of the plants by timely detecting diseases.
Suggesting precautionary measurements in order to enhance quantity and quality.
Crops can moreover be saved from variegated insects by timely spraying of pesticides
and ultimately increasing the quantity of the product.
The factors to be considered are temperature and humidity that leads to soft-hued the changes
in the health of the plant. The Changes that a plant undergoes are
captured by
the camera and analyzed with the image processing. The process of capturing image and the
required
environmental factors are washed-up with the IoT network. A storage device can be used to store all
the
required data’s for the wringer .In Image processing section, initially the image is captured from
the
camera and remoter the image is processed using the size, verisimilitude and shape of the image. It is
compared with the previous image and trammels the size, verisimilitude and shape of plant through image
processing and then shows result of plant growth in farmers using as plant growth is
increased
or not.
Changes in the verisimilitude of plant tissue are a worldwide symptom of plant disease. Often these
color changes are brought well-nigh by the yellowing of normal untried tissues .The verisimilitude sensor senses the
color of the leaf under consideration which is flipside parameter that is stuff used to determine
whether the leaf is either diseased or healthy. The sensor records values for, Red, Green and
Blue value of the leaf. The disease images of respective crops are saved in database. The humidity
and temperature values are moreover unauthentic on the plant diseases when these values are increased and
decreased. The verisimilitude values that are recorded for the leaf are then sent to the database for analysis.Later the obtained values of RGB, shape and size are compared with the threshold value in dataset to determine whether the leaf is diseased or not.
The most important barrier that arises in traditional farming is weather change.The number of effects of weather change
includes
heavy rainfall most intense storm and heat waves, less rainfall etc. due to these the productivity
decrease to the major extent. To overcome weather problem our project shows the live weather
status. To check the weather, click on the weather link that shows the web page to display the
weather status by entering the city name. The live weather status shows the live temperature,
live
clouds data that is sky is clear or not. This system reduces wastage of water, fertilizers and
increases
the plant quality.
It shows the fertilizers for the respective crops and plants. These fertilizers are stored in
database. Fertilizers and watering messages are displayed on farmer’s android app after fifteen
days
or after one month depend on the crop type and crop duration. The message is automatically
displayed week wise or month wise. Due to this the wastage of fertilizers are reduced.
When the farmer selects the crop
type and stated the sowing the weekly and monthly basis message is displayed on farmer’s
android
application.
Smart farming systems reduce waste, modernize productivity and enable
management of a greater number of resources through remote sensing.
In traditional farming methods, it was a mainstay for the farmer to be out in the
field, constantly monitoring the land and condition of crops. But with larger and
larger farms, it has wilt increasingly challenging for farmers to monitor everything
everywhere. This is expressly true with micro farming, where many remote plots
of land may be farmed for variegated crops, requiring variegated conditions and
precise tenancy of soil and water.
Today, the combination of smart irrigation and control being linked to local
sensors, as well as sensing for pH and other environmental conditions, including
insolation and local temperature, can stave off many issues that traditionally had
been rumored for by "walking the field." Remote monitoring through smart
farming systems enables production yields to increase considering farmers have
more time to shepherd to their farm's real issues: applying their expertise to solving
problems with pests, watering in any location, amending soil conditions -- all
through the use of sensing and automation. In a smart farming system, it's about
managing the supply of land and, based on its condition, setting it along in the
right growing parameters -- for example, moisture, fertilizer or material content --
to provide production for the right yield that is in demand.
During production, it's well-nigh managing one's resources to modernize the growing
process. Example is water,
through the use of precision water delivery, such as trickle or subsurface
methods, to reduce evaporation and to modernize soil moisture content, delivering
water only when it is needed through the use of sensors and automation.
Overall, the unshortened process from sublet to table is software-managed and sensor-
monitored, reducing overall costs, improving overall yield and quality of the
supply, and ultimately the wits for the consumer.
Crop diseases are an important problem, as they rationalization serious reduction in quantity
as well as quality of threshing products. An will-less plant-disease detection system provides
well-spoken goody in monitoring of large fields, as this is the only tideway that provides a
endangerment to discover diseases at an early stage. The solution includes a set of cameras
and computing hardware installed on a drone. The computer vision cadre system inspects image
spritz from cameras, detects diseased leaves, and performs classification. The inspection
results can be provided in various ways. Our solution of streamlined early disease detection
is based on an strained neural network, which is now the most robust technique for image
classification. The main advantages of our solution include upper processing speed and upper
nomenclature accuracy. A plant disease recognition system can recognize abnormalities on the
leaves e.g. mold.
Final deliverable will be a combination of software and hardware.
Hardwares to be used are Raspberry Pi 3 model B+ , temperature sensor,
humidity sensor, soil moisture sensor, 3G module, GPS locator, pressure
sensor, DSLR, Quadcopter and a smartphone to run the mobile app.
Softwares to be used are Codeblock IDE to requite commands to Rasberry Pi,
Android Development Kit for mobile app, MATLAB to trammels diseases of
plants and Sublime Text for web view. Using mobile app the farmer or
end user will enter five parameters to the system i.e. stage of sowing,
yield name, type of the soil, pH of the soil and undercurrent of the fields
or area. By stage of sowing, humidity sensor and GPS locator, the system will
predict the present climate conditions of the area, climate conditions include
stereotype intensity of sunlight, stereotype rain, stereotype speed of wind
self-glorification and humidity stereotype in that area. Soil moisture sensor
will help in turning on/off the water pumps automatically i.e. if water is
unelevated the specific range then water pumps will be automatically turned
on and without the desired level is approached pumps will be automatically turned off.
However, farmer can turn water pumps on/off manually too. 3G/2G module will be used,
considering there is no service of Wi-Fi in the rural areas which is our region of
interest but scrutinizingly all the rural areas are provided with 3G/2G services and
most likely we'll use a 3G module, to upload all the required information and pictures
of the plant leafs to the cloud. In order to make water irrigation increasingly trustworthy
we'll use two-way hallmark to turn on/off the water pumps, one is through soil moisture sensor
and the other method is to sense water pressure and a pressure sensor will facilitate to do so
which will be deployed at the nozzle of the water pump and without the required water is
approached the pumps will be automatically turned off. A Quadcopter with a DSLR
implemented on it will be used to capture pictures of the leafs through variegated
angles in order to determine diseases of the plants. First of all, DSLR will capture
pictures of the plants, 3G module will squire to upload these pictures to the cloud,
variegated techniques will be unromantic on these pictures to proceeds zone of interest
and then these pictures will be compared with the normal leafs in order to find the wandering
or disease in the leafs of the plnats and all this work will be washed-up using MATLAB.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 67630 | |||
| Raspberry Pi 3 model B+ | Equipment | 1 | 7180 | 7180 |
| SamSung sd card 32 GB | Equipment | 1 | 1550 | 1550 |
| Soil Moisture Sensor | Equipment | 1 | 350 | 350 |
| Soil Moisture Sensor | Miscellaneous | 1 | 350 | 350 |
| Temperature Sensor 18b20 | Equipment | 1 | 350 | 350 |
| Temperature Sensor 18b20 | Miscellaneous | 1 | 350 | 350 |
| Water Flow Sensor | Equipment | 1 | 650 | 650 |
| Water Flow Sensor | Miscellaneous | 2 | 650 | 1300 |
| Rain Sensor | Equipment | 1 | 300 | 300 |
| Rain Sensor | Miscellaneous | 1 | 300 | 300 |
| Water Pump 12 V Submisible | Equipment | 1 | 450 | 450 |
| Water Pump 12 V Submisible | Miscellaneous | 2 | 450 | 900 |
| Canon 1300 D | Equipment | 1 | 42500 | 42500 |
| Drone | Equipment | 1 | 8000 | 8000 |
| SamSung sd card 32 GB | Miscellaneous | 2 | 1550 | 3100 |