Agriculture Research

1. Smart Cultivation by Remote Monitoring
Puspendu Paul
B.Sc Agricultural Engineering Student
Sylet Agricultural University
City, Country
email address or ORCID
Sujit Biswas
Computer Science and Engineering Department
Faridpur Engineering College)
Faridpur, Bangladesh
sujitedu@fec.ac.bd
Abstract—With the advancement of modern technology, tra-
ditional agriculture is drastically changing, especially with the
utilization of Information and Communication Technology (ICT).
Ubiquitous sensors and the Internet of Things (IoT) are being
used independently for helping the farmers to understand better
the condition of overall field condition targeting to monitor soil
characteristics, climatic conditions, humidity, temperature, etc.
All these sensors and systems work individually and produce dif-
ferent data that requires analysis to analyze the data physically.
The typical process is time-consuming, and farmers should have
technological knowledge. Contrary, most of the farmers are not
technologically advanced to understand the term. A ready-made
result can help farmers to make quick decisions.
In this paper, we have developed a remote field monitoring and
controlling system with IOT. Farmers will visualize the processed
and analyzed data and give the command for taking further steps.
It increases production and better management through real-
time remote monitoring systems and nursing automation such as
irrigation, pesticides distribution, etc. The overall system records
every successful case, and machine learning-based prediction
helps further nursing guidelines provide field condition data.
Index Terms—Smart Agriculture, Internet of Things, E-
Agriculture
I. INTRODUCTION
Precision farming can be defined as the art and science of
using advanced technology to improve crop yields. Human
observation alone is not enough to solve the problem. For
example, precision farming was invented. A key idea behind
the new farming methods is to use technology. Use of IOT
devices has become frequent these days on agriculture sector
[1]. The Internet of Things (IoT) has found applications in
various fields, such as: Network industry, smart city [2], smart
home , smart energy, network car [3] and smart agriculture
[4] and other areas. The goal of IT is to integrate the physical
world with the virtual world by using the Internet as a
means of communication and information & data exchange
[5]. Smart agriculture technology and precision agriculture are
becoming more and more attractive due to their potential to
meet growing demand and meet global food demand. Smart
agriculture technology involves the integration of agricultural
technology and data-driven applications to increase yields and
food quality.
WSN technology (Wireless Sensor Network) is a key driv-
ing force for precision agriculture. The latest developments
in wireless communication and electronic technology have
enabled the design and manufacture of low-cost, feature-rich,
and energy-efficient small sensor system that can transmit data
over short distances. Inexpensive smart sensors that can be
connected wirelessly and They are used in large quantities
and provide huge possibilities for monitoring and controlling
houses, cities and the environment [1]. Over the years, wireless
sensor network (WSN) technology has developed rapidly [6].
Sensor nodes can be used to monitor larger cultivated land, and
these sensor nodes can wirelessly send data to the receiving
gateway. WSN can be used in various parts of agricultural
applications, such as plant health prediction, nutrient supply,
disease detection and irrigation. Climate planning and moni-
toring [1]. Using the IOT in agriculture can help farmers to
better monitor the field conditions. Proper use of sensors and
generating field level data, processing the data on cloud based
server, using LoRaWan technology are happening worldwide
[7]. Demand for new technologies to ensure world food
security. Traditional agriculture is usually run by families or
villages with rich agricultural experience. Smart agriculture
technology and precision agriculture are becoming more and
more attractive due to their potential to meet growing demand
and meet global food demand. Smart agriculture technology
involves the integration of agricultural technology and data-
driven applications to increase yields and food quality. When
problems arise, humans are the main field observers and
solution providers. However, this traditional method is no
longer applicable because agricultural products are highly
dependent on environmental conditions (such as climate and
water) and global warming issues (causing frequent droughts
and floods), and plant disease outbreaks can reduce agricultural
productivity. Human observation alone is not enough to solve
the problem [8] [9]. It is quite impossible for single person to
handle this much data and here comes the future possibility
of applying machine learning on agriculture. The emergence
of machine learning along with high-performance computers
and big data technology has created new opportunities for
big data science in the interdisciplinary field of agricultural
technology [10] [11] . By applying machine learning to sensor
data, the farm management system can be transformed into a
true artificial intelligence system that provides more detailed
recommendations and ideas for future decisions and actions,
and ultimately production improvements. The use of machine
learning models will become more widespread, enabling inte-
grated and usable tools. The learned data can be used in future
[12]

2. Although several types of works has been done on such field.
We have tried to discover a new way to apply the gained
knowledge so far.
II. RELATED WORKS
Huge works have been done to improve the crop yield and
implementation of modern technologies on field.
A generic prediction system for different crops to predict
disease was discussed in [13], they also proposed a software
based controlling platform for precision agriculture platform.
The system follows a layered approach. More specifically, the
system architecture is composed of 3 layers: a front-end layer,
a middle layer, and a back-end layer [14].
A suitable prediction system was developed in [15] in which
environment sensors to predict the growth and production
amount of field. Also in [16] a suitable cost efficient way
to apply sensor based drip irrigation has been proposed. In
which they used a rove-based single moisture sensor instead
of using multiple sensors. The database included automated
system analyses the water requirement of plant. A predefined
soil moisture values are given in the database and according
to the soil type it is editable. Different types of soils unique
values are different, they will have different moisture values.
In [17] a suitable field monitoring network was reviewed
which was based on ARM core.The system helps to monetiz-
ing the irrigation technology or proper irrigation. The ARM
cored and GSM used for having a more precised control on
the irrigation on field [18]. The system also able to send coded
signals to the receiver. In [3] has identified several advantages
and challenges of the Internet of Things. We will introduce
how the IoT ecosystem and the combination of IoT and DA
realize smart agriculture. In addition, we put forward future
trends and opportunities, divided into technological innova-
tion, application scenarios, business and competitiveness.
As discussed in [8] in order to effectively increase crop
yields, the environmental conditions in the field and its sur-
roundings must be controlled. In order to increase productivity,
soil parameters, weather conditions, humidity, temperature,
etc. must be properly controlled. The Internet of Things (IOT)
is used for various real-time applications.The introduction of
the Internet of Things and sensor networks in agriculture has
restored traditional farming methods. Using IOT for online
crop monitoring can help farmers keep in touch with the field
anytime, anywhere. The work of [19] project is based on
the IoT-based Autonomous Wireless Sensor Network platform,
which includes a soil moisture sensor (MC), a soil thermome-
ter, an ambient temperature sensor, an ambient humidity meter,
a CO2 sensor, and the device’s daylight intensity (related to
light) Resistance) to collect information about the farm in
real time through multi-point measurement. The [20] article
proposes a practical method to obtain plant temperature,
humidity and soil moisture data. To this end, researchers
developed a prototype and an Android application to collect
physical data and send it to the cloud. In [21], it introduces a
linked data platform for publishing sensor data and linking it
to existing resources on the Semantic Web. A related sensor
data platform named Sense2Web manages edible and inter-
operable descriptions, and links various sensor data ontology
with resources described in the Semantic Web and Data Web.
The latest developments in (wireless) sensor networks and
the ability to produce inexpensive, energy-efficient sensor
hardware have sparked great interest in integrating data from
the physical world into the Internet. Access to sensor data can
provide a wide range of applications in different fields, such
as geographic information systems (GIS), healthcare, smart
homes, and business applications and solutions. In this [22],
we will focus on publishing relevant data to annotate sensors
and link to other sensors. Existing resources in the network.
The remote monitoring system based on LoRa / LoRaWAN
network has been described in [23], [24], [25]. The paper
[23] introduced the draft of the LoRa-based IoT monitoring
system for the cannon plantation. The end node of the system
uses the Arduino Uno card to connect the pH sensor and
the soil moisture sensor to monitor the soil condition, and
uses the Dragino shield to connect the LoRa module to
the Arduino Uno board. Your settings and upload them to
the server through a single-channel gateway. The system
achieves a communication distance of 700 meters, and the
parcel delivery rate is 40.9percent. Literature [24] proposed a
personalized information monitoring system based on LoRa
network protocol. The combination of LoRa and NB-IoT
technology can increase the data transmission range [25].
Each terminal node uses the LoRa protocol to transmit data to
the gateway.The gateway is connected to the NB-IoT module
to transmit data to a remote location via the Internet.
Two prototype sensor nodes have been established, which
can be operated wirelessly via far-field radio technology and
a dedicated RF power supply in the 868 MHz ISM frequency
range [26]. This article discusses the actual implementation of
wireless sensor networks to upgrade cyber-physical systems
used in structural condition monitoring applications in the
construction industry. The network consists of a mesh network
of battery-free LoRaWAN radio detection nodes that collect
and exchange data. Connect sensor nodes to nodes in the
digital world via the Internet [26][27]. The [28] propose a
programming algorithm to connect a large number of IoT
devices in LoRaWAN. The proposed algorithm supports time
synchronization transmission to reduce scalability issues due
to random channel access used in LoRaWAN.
By applying machine learning to sensor data [10], farm
management systems are being transformed into real-
time programs with AI support that can provide valuable
information and guidance to support farmers’ decisions and
actions. The methods of machine learning are theoretically
reasonable and can work well with more or less manual
test data sets. However, they are based on your ability to
understand real data [11]. The machine learning model
of drought-related variables based on remote sensing
data was compared with Kriging’s spatial interpolation.
Two performance counters are used. One of these is the
manufacturer’s drought accuracy (defined as the number of
samples correctly classified as extreme, severe, and moderate

3. drought categories based on the total number of samples in
those categories), and the drought accuracy of the other user
(defined as the According to the total number of samples
allocated to the drought classification, it is correctly classified
into the drought classification [29].
Big data management Deep learning will achieve greater
success in the near future, because it requires very little
manual design, so you can easily take advantage of the
increased amount of available data and calculations [30]. The
main impact of [31] is to prove the superiority of machine
learning algorithms and more comprehensive intelligent
systems in the current precipitation prediction methods
using precipitation derivatives. These results indicate that
machine-based learning systems have a positive effect. The
intelligent system must predict rainfall based on the accuracy
of the prediction and the least correlation in all climates.
Different crops have their own leaf wetness reading for their
disease-warning systems [32].
III. PROPOSED SYSTEM
A. System Overview
How you will collect data from field?
The essential data will be collected from the field sensors.
Temperature reading, Leaf wetness reading, soil moisture
reading, solar radiation reading, leaf greenness reading, Wind
direction and additional new types of sensors can be set to
get additional data collection. Sensors will be mounted on
stands(Temperature sensors, canopy sensors, smart cameras
etc.) or attached with plants (Leaf wetness sensor) or set
under soil (Soil moisture sensor).
How collected data will be sent to server?
Sensor’s data will transmitted to the server through gateway.
Using high speed internet, LoraWan gateway on field to con-
nect the sensors and a network switch data will be transmitted
to the cloud.
What kind of data will be collected ?
Different types of data are needed to monitor the field.
Temperature data, humidity data, soil moisture data, solar
radiation data, leaf wetness data, Vegetation data, air quality
data, soil moisture, wind direction data, ph meter sensor
for collecting ph data. Moreover satellite data can also be
used as remote sensing offers privileges with lower temporal
resolution. Needed data for crop phonology, vegetation etc.
can be collected directly from satellite data server.
How result or targeted result will be evaluated?
Collected data will be sent to the cloud and with further
processing and manipulating with different servers and related
algorithm. Prediction server, satellite data server, weather
server, central processing server will do combined work to
give a suitable and reasonable output to the user. User will
have access to the field condition monitoring with his phone
and will be able to take necessary steps.
The idea is to develop a system that will help the farmers
to control the field remotely with smartphone.The system
Collect sensor data
Data analysis &
Prediction server
Yes
No
Yes/No
Finish Operation
Motor driver server
Phone
On field machineries
Server For data analysis
Avilable Sensors
Unmanned machines
Smartphone application for decision making
Fig. 1. working principle of the system
will also warn the farmers about important field events like
irrigation timing, probability of disease of plants, Processed
satellite image, crop harvest prediction etc. Needed data of
fields will be stored in cloud for further analysis and better
prediction of future. Automated irrigation, disease prediction,
real-time surveillance data will be processed by central server
and provided to the farmer on the smartphone, the provided
data will be saved for better prediction on future. In this system
the farmer will be able to control and visualize the whole
working process and important data with the smartphone and
the app will also provide the prediction of irrigation timing,
disease attack, leaf wetness, NDVI data to Farmer for taking
physical action. We used LowRaWan for
B. Data Collections
The system will provide 5 types of data. Disease detection,
Irrigation, real-time surveillance, cloud service, smartphone
app. The system will work according to field sensors, collected
data processing and manipulation servers, sending processed
data to smartphone.
•
•
• Also write the purpose of the data.
What kind of devices are utilized to collect data from
field?
On field level the given sensors can be used:
1 Air Temperature sensor
2 Soil Temperature sensor
3 Soil Moisture Sensor
4 Rain meter
5 Wind speed
6 Leaf Wetness sensor
7 Solar radiation sensor

4. 8 The Smart Cameras
Write each devices and their collected data in brief
The air temperature sensor is a thermistor, which means that
its resistance changes according to changes in temperature.
It acts like a coolant sensor. The PCM applies a reference
voltage (usually 5 volts) to the sensor, and then checks
the received signal voltage to calculate the air temperature.
Soil temperature is an important factor affecting the life
of underground plants, which will affect root growth,
respiration, nitrogen decomposition and mineralization. The
IOT sensor can estimate the floor temperature by measuring
air temperature and other factors. However, the most accurate
measurement method is to use an embedded probe. Depending
on the root structure of the plant in question, multiple probes
can be installed at different depths. Another IoT sensor using
infrared technology can be used to monitor the soil surface
temperature.
Data format Modern thermometers can measure infrared
radiation or resistance. Modern thermometers usually produce
digital data that can be directly input to a computer.
The soil moisture sensor uses a container to measure the
dielectric constant of the environment. In soil, the dielectric
constant depends on the moisture content. The voltage gen-
erated by the sensor is proportional to the dielectric constant
and therefore proportional to the soil moisture. IoT and Smart
Agriculture technology can measure following things:
• Soil moisture
• Conductivity.
• Volumetric water content.
• Soil water potential.
Fig. 2. Leaf wetness sensor
Data format The sensor produces an output voltage
corresponding to the resistance by measuring how we
determine the moisture content.
LWS(Leaf wetness sensor) approximates the thermal
mass and radiation characteristics of the blade to accurately
simulate the moisture conditions of the real blade. The
principle of operation is simple: if the canopy is wet, the
sensor is wet; if the canopy is wet, the sensor is wet. When
the canopy dries, the sensor dries out. LWS measures the
dielectric constant at the top of the sensor.
Fig. 3. Leaf wetness sensor
The solar radiation sensor absorbs solar radiation and offers
a flat spectrum from 0 to 1500 W / M2. Pyranometers detect
both direct and diffuse radiation. The radiation absorbed by the
sensor is converted into heat. This heat then flows through the
sensor to the housing of the devices. IoT sensors can measure
different types of solar radiation that play an important role in
photosynthesis. In addition to the basic Lux illuminance level,
the Internet of Things can also measure the following:
• Solar – Photosynthetically active radiation.
• Solar – UV.
• Solar – Shortwave.
Fig. 4. Leaf wetness sensor
Weather stations provide a lot of data related to weather
conditions, which is important when correlating patterns with
related data. The rain sensor for the expansion disc works
through a pressure gauge connected to the sprinkler system.
The disc in the meter absorbs water and expands when more
rain falls. This will send a message to the sprinkler system
controller and cut off the flow. Activate the signal of the
sprinkler head.
• Precipitation (optical and tipping bucket measurements).
• Temperature.
• Humidity.

5. • Air pressure.
• Wind speed.
• Wind direction.
Fig. 5. Leaf wetness sensor
In these system, machine learning will play a significant
role. The server generated data will be stored for future use
and predicting the upcoming event on other fields those are
connected with the servers following the same principle.
Sensors like air temperature sensor, soil moisture sensor,
rain meter and used other sensors has different data types
and the measure their data in a different parameter[33].
Temperature data, soil moisture data, rain data. wind-speed,
leaf-wetness, solar radiation [22]. The sensor will collect data
and send them to the server. server will calculate, manipulate
and generate proper prediction and visualisation o data, these
data will be sent to the smartphone of farmer. Farmr will be
able to command and take the necessary steps according to
the servers prediction. Full system will be consolable through
smartphone.
C. Useing LoraWan
LoraWan will be used to connect the field sensors. This
technology is cheap as well as has versatile operations. To
connect the field sensors with the central server, gateway and
lorawan will be used with different security protocols and
improved functionality to keep the cost low. The sensors will
send data through lorawan to gateway, then the gateway will
transmit data to the central server [34]. In short Lorawan will
be a helping hand for more secure connectivity with sensor
to gateway and server at a minimum cost.
D. Prediction system
Prediction systems are being more and more useful with
the help of IOT. Several types of prediction system is
available like irrigation prediction system, disease and pest
prediction system. A early warning system of disease has
been researched and developed to prevent and reduce the risk
of diseases in crops.
Irrigation is a vital part for crop production.Lack of water
or excess of water, both can make a catastrophic result. To
ensure proper irrigation with an exact amount of water proper
sensor data collection is needed (leaf wetness, soil moisture).
Thus a properly functional IOT based irrigation prediction
system can help us to better handle any type of calamity.
Predictive modeling can help us in here. This is a way of
using big data to create models that predict revenue status and
revenue volume. In short, predictive analytics uses business
intelligence to first collect, integrate, and analyze data from
large server farms. Models are established to predict the
conditions under which pests are most likely to invade and
enable farmers to make accurate decisions about when and
where crops need pesticides.
The data comes from various sources. Most of the data
was collected on-site by the International Maize and Wheat
Improvement Center (CIMMYT) and its partners in Ethiopia
and Tanzania. The rest of the data comes from public images
found in Google Images. For this issue, in addition to the
provided data, the use of external data is also prohibited.
Below are some examples of data listed by categories, namely
healthy wheat, leaf rust and trunk rust.
First, building a predictive model requires a lot of data.
Second, regions in the world lacking infrastructure to support
IoT solutions and smart agriculture are still unable to take
full advantage of the full capabilities of the technology. In
addition, a thorough investigation is required to find the best
way to prevent pest infestation. Depending on the research
required, progress may be slow and costly.
E. Use of Deep learning for future case
Machine learning is a data analysis technology that can
automatically create analysis models. This is a branch of
artificial intelligence. The idea is that the system can learn
from data, recognize patterns and make decisions with minimal
human intervention. The system will learn from the field
conditions simultaneously. In most cases, machine learning
methods are used in the plant management process and then
in the management of agriculture and animal conditions. In
agriculture, they are used to predict the yield and quality of
crops and the yield of animals. The machine learning process
begins by entering training data into the selected algorithm, as
the training data is known or unknown data, in order to develop
the final machine learning algorithm. The way you enter the
training data affects the algorithm, and this concept is covered
briefly later. When you test that this algorithm is working
properly, the new input data is fed into the machine learning
algorithm. The prediction and the results are then checked. If
the prediction is not as expected, the algorithm is retrained
several times until the desired output is found. This allows
the machine learning algorithm to continuously learn by itself
and produce the most optimal response, the accuracy of which
gradually increases over time. Naive Thomas Bayes conjointly

6. assumes that the attributes are not absolutely independent. Real
data sets will ne’er be fully independent, however they’ll be
closely joined together. Therefore, compared with supplying
regression(Logistics Regression), the naive Bayesian technique
features a higher systematic error, but a lower variance. If
the information set matches the bias, then Naive Bayes is
that the best classifier. The main difference between the
models you build for a ”feature” reading purpose is that Naive
Bayesian treats them as independent, while SVM occurs in
the interactions between them to some degree as long as you
have a nonlinear kernel (Gauss , rbf, poly etc.). SVM tries
to maximize the distance between the closest support vectors,
while commission regression maximizes the likelihood of the
rear category. For the kernel space, SVM is faster. Decision
Trees are terribly flexible, easy to understand, and straight-
forward to debug. they’ll work with classification issues and
regression problems. therefore if you’re attempting to predict
a categorical price like (red, green, up, down) or if you are
trying to predict a nonstop value like 2.9, 3.4 etc call Trees
will handle each problems. in all probability one in all the
best things concerning call Trees is that they solely would
like a table of knowledge and that they will build a classifier
directly from that data without having any up front style work
to require place. To some extent properties that don’t matter
won’t be chosen as splits and can get eventually cropped
therefore it’ terribly tolerant of nonsense. to begin it’ set it
and forget it.
Naive Bayes is employed a great deal in artificial intelli-
gence and laptop vision, and will quite well with those tasks.
call trees perform very poorly in those situations. Teaching a
choice tree to acknowledge poker hands by wanting a legion
poker hands does very poorly as a result of royal flushes and
quads happens so very little it usually gets pruned out. If it’
pruned out of the ensuing tree it’ll misclassify those vital hands
(recall tall trees discussion from above). currently simply
assume if you’re attempting to diagnose cancer exploitation
this. Cancer doesn’t occur within the population in giant
amounts, and it’ll get cropped out additional likely. excellent
news is that this may be handled by using we have a tendency
toights therefore we weight a winning hand or having cancer
as beyond a losing hand or not having cancer which boosts it
up the tree so it won’t get pruned out. once more this is often
the a part of standardization the ensuing tree to true that is
mentioned earlier.
IV. SYSTEM ARCHITECTURE
1 The system will collect necessary data from the field
and then for further data analysis and processing in order
to predict the upcoming field event like irrigation timing,
weather prediction or disease prediction. The server will
complete the job with it’s programmed algorithm and notify
the decision maker (smartphone) and wait for next command.
If the decision maker makes affirmative decision then the
motor driver server will send the IOT connected field
machinery and equipment about further action and then the
process will be finished. In case of negative command by
decision maker, the process will finish without taking any
steps by field machinery. The notification by the server will
be active unless the farmer takes any step to mollify the field
condition. Support Vector Machines (SVM) is way over for
Random Forests (RF). this implies that coaching a SVM are
going to be longer to coach than a RF once the scale of the
training knowledge is higher.
A. testbed setup
Describe how the sensors are setting up in the field
Network Gateway
Sensor Data
Sensor Data
Sensor Data
Sensor Data
Sensor Data
Raw Data
Processed data (Prediction, visualization)
Information
Central Management Server
Precision Agriculture Machines
Digital Signal
USER
Database
Machine Learning Server Prediction Server
Geo Server Weather Server
IOT Gateway
Command
Fig. 6. System Architecture
Sensors are settled on field on a stand based network on
which particular stands will contain the sensors (just like
weather stations) and collect data. The stand will represent
an unique latitude and longitude. Sensors will be attached to
it. As per the sensor’s type, it will be placed on over soil
(LWS) or under soil(soil moisture sensor). All sensor will get
power from a common power source with power cables.
On Field level, Sensor will be connected with the system
with wire-less connection. The collected data will be sent
through gateway, providing an IP address to the server.
Gateways are routers equipped with LoRa hubs, through
which they can receive LoRa data packets. This provides
greater flexibility for gateway administrators. The system will
use lorawan as it is based on spread spectrum modulation
technology derived from spread spectrum chirp technology.
Fig 1. Better to draw figure with icon and system architecture
At processing unit, a dedicated server will manage all
type of data and apply algorithms. To predict different
events like weather, disease attack, irrigation, wild animal
interruption etc. there will be different prediction server.
For future prediction, predicted conditioned data will be
saved on a database. The machine learning process starts by
inputting training data into the selected algorithm, because

7. Command centers (Smartphone)
Field Sensors Cloud Server Field Machineries
Prediction servers
Weather servers
Geo Servers
Database
Central Processing
server
Unmanned
Machineries
Countermeasures
Automated
Equipment's
Field sensors
Areal Imagery
services
Smart Cameras
Fig. 7. System Flowchart
the training data is known or unknown in order to design
the final machine learning algorithm. Check if this algorithm
is working properly. The new input will be passed to the
machine learning algorithm. Then review the predictions and
results. If the prediction does not meet expectations, the
algorithm is retrained several times until the desired result
is found. This enables the machine learning algorithm to
continuously learn on its own and obtain the best answer, and
its accuracy will gradually improve over time.
Geo spatial data will be collected form GeoServer. GeoServer
is an open source server written in Java, users can use it to
exchange, process and edit geospatial data. Developed for
interoperability, it uses open standards to publish data from
all major geospatial sources. Weather data will be collected
form Weather servers. Weather server provides hydrologic,
weather and climate forecasts and warnings for a region,
adjacent waters and ocean areas.
Computer network server will manage the networking sides
of the system The system collects environmental data directly
from environmental sensors, ground sensors and video
surveillance cameras. In order to create an independent
system, we use solar panels to run the system without power.
To indicate the location of this system for global positioning.
Module installed in the system (GPS)
In figure the working principle of prediction server has been
shown. In here processed data will go to the Trained ML
algorithm. From there a temporary prediction will be made. If
the prediction is true according to the condition of field then
the process will announce a final prediction. If the prediction
becomes false, then the retrain algorithm will be activated.
After collecting the data, the data/facts needed for processing
from the place of origin to the computer will be retrieved.
The collected data is converted into a machine-readable
format through an input device, and then sent to the machine.
Data in a more meaningful form (information) in the CPU.
Data training
Process
Trained ML Algorithm
New processed data
Prediction of
the event
Correct output result
with Exact prediction
Retrain Algorithm
False result
Data training process
Fig. 8. Machine Learning Algorithm Processing flowchart
Encoding
Data Capture
Data Collection Data Transmission Data communications
Performing as instruction Transform Raw Data into Information
Sorting Data Retrieve Data
Decoding Presenting Data to user
Input Stage
Processing Stage
Output Stage
Storage Stage
Stages
of
Data
Processing
Cycle
on
Computer/Server
Fig. 9. Data Processing circle on Server
The result is the creation of necessary information that can
be entered in the future. In the data collection process, the
data is called in a computer-related form at the original
location (the original document itself is prepared to be input
in a machine-related form). After the data is collected, the
original data will be transmitted to the ”processing center”,
decrypted, converted from one medium to another, and finally
stored on the computer.
V. RESULT AND DISCUSSION
Analysis Graph and Discussion write in here
1) : The key parameters those comes first while monitoring
crops are temperature, moisture, humidity, nitrogen,
potassium. These parameters varies from crop to crop
and field environment type. The N emissions from organic
nitrogen supplies are controlled by the soil environment.
Incubate the soil to assess the consequences of soil wetness
(50, seventy and 90percent water retention) and temperature
(10/15, 20/15 and twenty five ° C ./20 ° C [14/10 h]) and
unharness nitrogen from four sources of organic nitrogen.
The mechanics of nitrogen release from the nitrogen source
is decided by frequently mensuration the nitrogen content of
ammonia and nitrate over twelve weeks. N is higher within

8. the following order: organic compound (91-96percent)¿ feed
(BM) (56-61percent)¿ alfalfa granules (AP) (41-52percent)¿
partly composted manure (CM)) (37 - 45percent). The
increase in soil wetness redoubled cyber web N discharged
by AP and CM by 12percent and 21percent, respectively,
however had no important result on the net nitrogen released
by organic compound and BM. web nitrogen released by
AP, BM and CM by 25percent, 10percent and 13percent,
respectively, but has no significant effect on the net nitrogen
released. The results show that soil moisture and temperature
have totally different effects on the supply of nitrogen in
nitrogen - Contains organic substances, counting on the
nitrogen source. during a greenhouse production system
which will management irrigation and temperature, fertiliser
management should take into account each the N supply
and also the soil environment. The usage rate of organic gas
materials are often improved [35].
Controlled release fertiliser (CRF) is one amongst the
most effective plant management practices in American state
associated could be a soluble fertilizer (SF) coated with
a polymer, resin, or sulfur-coated carbamide mixture. The
accelerated temperature controlled culture (ATCIM) technique
is employed to predict the discharge of N (N) in CRF for
restrictive purposes. to see the quantity of N release within
the CRF field, an on-the-scene method that needs multiple
samples and is costly, admire bag field study, is used. If
ATCIM may be accustomed predict the release of CRF-N in
the sector, compared with the field bag method, growers have
a quicker and cheaper technique to see the discharge of N [36].
Fertilizer salts are sensitive to the humidity of the encircling
air; on top of bound ratio , they’re going to absorb water. The
humidity above that they absorb water is outlined as crucial
relative humidity (CRH). The plant food world organisation
leading to most growth decreases with increasing tempera-
ture, whereas the growing medium EC resulting in maximum
growth is analogous the least bit 3 temperatures. for many
greenhouse crops. vital interactions of soil wetness fertilizer
level (p¡0.01) showed that application of fertilizer has no
significant effects at twenty five and 50percent soil moisture
content.
2) : The pie chart shows the percentage of damaged crop
and percentage of fresh crop after implementing the system.
Here we can see 83.5percent are fresh crops, 13.9percent
crops has been somehow destroyed or damaged by insect/pest
attack, 2.6 percent crops are damaged by countermeasures or
for systematic failure. The bar diagram shows the crop-wise
damage rate. The 0, 1, 2 indicates crop type 1, type 2, type 3.
3) : Nitrogen helps plants create the proteins they have to
supply new tissues – particularly folio late tissues. once plants
don’t get enough nitrogen, they will flip yellow or pale green.
Phosphorus stimulates root growth, helps plants set buds and
flowers, and produce seeds. It conjointly helps plants use al-
ternative nutrients additional efficiently, and helps turn energy
Fig. 10. Caption
Fig. 11. Caption
from the sun into usable energy for your plants. Fertilizing
adds nutrients that your soil lacks and replaces nutrients that
plants took up with last year’s growth. Understanding the NPK
ratios of fertilizers can assist you select the best one for the
sort of plants which are being grown. On the graph, the density
of N, P, K has been shown. The data was collected from field
sensor and this graph was generated from real-time data. The
Y-axix represents the density and the X-axis represents the
max value of fertilizer application (Dose on certain time of
farming). There is a variation of each N, P and K need on the
land.
Fig. 12. N, P, K density depending on their max value on field

9. Fig. 13. Accuracy of different algorithms
Fig. 14. Estimated insect count, Number of week used, number of weeks
quit, pesticide used category, Number of doses, Crop damage for the system
implementation of malfunctioning of the system, soil type according to the
crop type, season
4) : In order to test the applied Machine learning algorithm,
different tests has been done. Decision tree, Naive-bayas,
SVM, RF. In our test, RF and Naive-bayas has greater accuracy
than the other algorithm. SVM has a lower accuracy. On
the graph x-axis represents the accuracy level of different
algorithms. The y-axis represents the name of the algorithms.
An accurate algorithm will provide results more perfectly.
5) : In this multiple plotting , we can see a bunch of data.
There are Estimated insect count, Number of week we used the
system and then turned off the system, pesticide used category,
Number of doses, Crop damage for the system implementation
of malfunctioning of the system, soil type according to the
crop type.
6) : The below figure shows the estimated data about insect
attack incident after quieting the countermeasures on the field
we can see for every type of crop (type 0, type 1, type 2), the
insect attack incident has been increased after all type of smart
sensors and countermeasures for preventing such events were
turned of. The rate increased overtime. The y-axis represents
the number of weeks for how long the system was closed and
the x-axis represents the insect count.
7) : In this graph the number of doses are shown. The x-
axis represents the number of weeks and the y-axis represents
the count o doses. ML generated prediction system was used
for dose distribution to the crop samples. The graph clearly
Fig. 15. Insect attack events after turning off the system for weeks
Fig. 16. Number of doses according to weeks
shows that the last weeks when the system activity was
reduced, the dose also reduced.
8) : This graph is showing the overall use of system and
system’s privileges. The x-axis indicates the week count and y-
axis represents the count of using system privileges. The count
of the system privileges use has been gradually increased to the
mid section and again after mid section gradually decreased
to the last weeks while the system’s works was intentionally
reduced.
APPENDIX
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