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Scheduling Method for Agricultural IOT Business Based on Improved Multiobjective Evolutionary Algorithm

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In the modern society where technology is advancing every day, the agricultural industry is also undergoing innovation, and the Internet of Things (IoT) based on machine learning algorithms adds new vitality and yields increasing directions to this ancient industry. This study analyzes and processes data based on improved multiobjective algorithms for the application of IoT in agriculture and establishes the relevant algorithmic models. The components of IoT are introduced, and it is determined that information flow, capital flow, logistics, and Internet are the main reasons why it can be generated. After establishing an improved multiobjective evolutionary algorithm model with good convergence and diversity, the embedded multichannel sensor collection device measured in this experiment in the same cultivated environment has a more stable collection data cycle compared to the external sensor. The embedded multichannel sensor has better stability, so this sensor is selected for this study to monitor parameters such as soil moisture content and oxygen content. The IoT requires timely communication and consultation among users, and the actual experiment found that the use of ultrashort waves with a frequency of 230 MHz is the most stable and efficient.
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Research Article
Scheduling Method for Agricultural IOT Business Based on
Improved Multiobjective Evolutionary Algorithm
Kewang Zhang and Zhixu Shu
School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang 464000, China
Correspondence should be addressed to Zhixu Shu; 2006270044@xyafu.edu.cn
Received 6 July 2022; Revised 1 September 2022; Accepted 5 September 2022; Published 22 September 2022
Academic Editor: Zaoli Yang
Copyright ©2022 Kewang Zhang and Zhixu Shu. is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
In the modern society where technology is advancing every day, the agricultural industry is also undergoing innovation, and the
Internet of ings (IoT) based on machine learning algorithms adds new vitality and yields increasing directions to this ancient
industry. is study analyzes and processes data based on improved multiobjective algorithms for the application of IoT in
agriculture and establishes the relevant algorithmic models. e components of IoT are introduced, and it is determined that
information flow, capital flow, logistics, and Internet are the main reasons why it can be generated. After establishing an improved
multiobjective evolutionary algorithm model with good convergence and diversity, the embedded multichannel sensor collection
device measured in this experiment in the same cultivated environment has a more stable collection data cycle compared to the
external sensor. e embedded multichannel sensor has better stability, so this sensor is selected for this study to monitor
parameters such as soil moisture content and oxygen content. e IoT requires timely communication and consultation among
users, and the actual experiment found that the use of ultrashort waves with a frequency of 230 MHz is the most stable
and efficient.
1. Introduction
roughout the history of human development, every great
progress of mankind in this long history has been an ad-
vancement in technology. e invention of the steam engine
marked the arrival of the first technological revolution, and
since then all mankind has evolved from the inefficient mode
of manual production to the efficient mode of machine
production [1]. e discovery and use of electricity set off the
second technological revolution, and the use of electricity
has again greatly improved the efficiency of human pro-
duction and life. Nowadays, we are experiencing the third
technological revolution, and the great leap forward in
computer technology has brought all mankind into the era of
big data [2]. e Internet of ings (IoT) was born on the
basis of the development of certain intelligent technology,
which has advanced mankind toward the era of intelligence
in everything [3]. e achievement mark of completing the
third technological revolution is the realization of the
Internet of ings all over the world, where anything is
connected to the World Wide Web at any time and place and
can interact intelligently with humans. If this vision can be
realized, then the lives of more than 7 billion people in the
world are radically changed, and to a large extent, the way of
thinking of human beings will also be changed. Leaving
behind the new technological crisis that could be brought
about, human life will enter a new era of efficiency and
beauty.
With the end of World War II, humanity entered de-
cades of peace and the global economy expanded to an
unprecedented extent. Human exploitation of the Earth’s
ecology has also become more thorough, and the negative
impact on the global environment has expanded more than
tenfold [4]. After the global financial crisis in 2008, the
United Nations proposed the slogan of green economy and
advocated the implementation of energy-saving and low-
carbon guidelines for all industries in the face of the
spreading economic downturn. And the emergence of IoT
Hindawi
Mathematical Problems in Engineering
Volume 2022, Article ID 7264882, 10 pages
https://doi.org/10.1155/2022/7264882
provides a viable answer to this problem, which has largely
matured in a very few developed countries and become a
target of the next decade’s plan for most developing
countries [5]. With its efficient and orderly nature, the IoT
can save natural and human resources and can greatly al-
leviate energy consumption. And so far, the core technology
of IoT has been largely completed, while researchers in
various countries are now optimizing and upgrading it. And
with the construction of telecommunication infrastructure
all over the world, smartphones and laptops are also popular,
and wireless network coverage is basically achieved all over
the world. is provides a basic soil environment for IoT to
take root in various fields, and agriculture, which has a huge
footprint and more backward tools, benefits from this to be
able to upgrade its business [6–8].
In both daily life and social production, human beings
have always strived to get the maximum benefit with the
minimum cost. e process of finding the optimal solution
by considering two conflicting aspects at the same time is
called multiobjective optimization. In fact, scientific research
and engineering practice are full of multiobjective optimi-
zation problems (MOPs), which are the most common
among the many types of problems that researchers need to
face on a daily basis [9]. Multiobjective optimization
problems are very common and important in engineering
applications and other real life. ese practical problems are
usually very complex and difficult and are one of the main
research fields. For example, when designing a particular
watering truck, agricultural implement companies generally
want to spray as wide as possible, with as much accuracy as
possible, with the lightest possible weight of the water tank,
and with the least possible consumption of gasoline and
diesel fuel. However, two of these design goals are clearly in
conflict, for example, a wider spraying range will lead to an
increase in gasoline and diesel consumption, so a com-
promise solution has to be found among these goals [10].
Since such problems are very common and frequent in
agricultural production, it is very important and urgent to
use multiobjective evolutionary algorithms in computer
technology to help farmers solve this problem. However,
improvements in these design goals may conflict with each
other, e.g., a longer range may lead to increased fuel con-
sumption, so a compromise must be made among the design
goals [11]. Since MOPs are widely available in the real world,
it is of great theoretical importance and application to study
multiobjective optimization algorithms to help people better
solve such problems.
e contribution of research innovation lies in the in-
troduction of advanced genetic algorithms to improve the
multiobjective evolutionary algorithm. Finally, an improved
multiobjective evolutionary model with good convergence
and diversity is established. After completing the algorithm
optimization, compared with the external sensors, the
embedded multichannel sensor acquisition equipment
measured in the same farming environment has a more
stable data acquisition period. And it can always be stable at
the maximum energy output and not lower than a pre-
determined threshold. e embedded multichannel sensor
has better stability, so the sensor is selected to monitor soil
moisture and oxygen content. e Internet of ings re-
quires timely communication and negotiation among users.
e frequency of ultrashort waves is mainly used for
communication between farmers, the transmission of
weather information, the water level of nearby reservoirs,
and other important information.
2. Structural Components of the
Internet of Things
2.1. Information Flow. Information is the basis for the es-
tablishment of the big data era, and it is the most funda-
mental element of the moment. Analogous to human beings,
information is equivalent to the nervous system, a structure
that sustains the normal functioning of society and plays a
vital role in the economic trade of goods circulation. In the
Internet of ings, the existence of information flow is the
primary condition for its existence. e world is full of
information, and it can be said that any medium that carries
certain content can be called information [12]. However,
information in the IoT refers specifically to market infor-
mation in economic relations. Market information in this
study includes the following: characteristics and situation of
economically related industries, relations of industries with
each other, size and consumption of each industry, number
and organization of industrial personnel, generation and
elimination of natural disasters, etc. Modern communica-
tion technologies allow for rapid, efficient, and wide dis-
semination of information under this concept in the flow of
information [13]. In the field of agriculture, accurate and
rapid transmission of information can provide strong
support and reliance on policy formulation, disaster pre-
vention, and sowing and harvesting. e smooth flow of
information is the basis for rational and orderly imple-
mentation of plans and is the cornerstone for the proper
functioning of the economy.
Information in the economic market does not generate
any profit per se, but it has value in itself, and this value can
be transferred to production tools after the purpose of
dissemination has been achieved, thus indirectly increasing
profits [14]. e rapid dissemination and effective use of
information can greatly improve management efficiency,
increase labor productivity, and ultimately make companies
and enterprises more profitable. And it also plays an im-
portant role in environmental protection and energy saving.
Information can effectively regulate the supply and demand
in the market and make energy mobilized in the direction of
gap goods in time to avoid unnecessary waste and loss [15].
“Market allocation mechanism of information resources”
refers to the process that the market automatically organizes
the production and consumption of information through
price leverage. at is to eliminate or reduce the uncertainty
in information market activities through the interaction and
mechanism of supply and demand, price, competition, risk,
and other factors in the market mechanism. ereby real-
izing the optimal configuration of information resources.
e market allocation mechanism of information resources
mainly uses market mechanisms, including supply and
demand mechanism, price mechanism, and competition
2Mathematical Problems in Engineering
mechanism to effectively allocate information resources.
is becomes the basic point of the Internet of ings that
differs from the traditional economy. A well-functioning
information flow can effectively reduce the cost of products
and make their value transferable and realizable.
2.2. Logistics. Logistics is the abbreviation of material cir-
culation, which mainly refers to the storage, packaging,
transportation, distribution, loading, unloading, and signing
of goods. After the popularity of the Internet, the rapid
development of e-commerce has led the logistics industry to
undergo a corresponding upgrade and replacement [16]. e
Internet has expanded the scope of logistics from local to
global, allowing users to choose any logistics company
online that meets their needs, and logistics companies to
receive more orders and operate in more regions. It also
gives rise to third-party trading platforms, which provide
open, transparent, and secure transaction information for
both buyers and sellers, allowing small and microenterprises
and individual merchants to expand their business to dozens
or even tens of times the previous range. Web-based logistics
offers higher value and more opportunities for both buyers
and sellers and has given rise to third-party companies that
provide this service. In areas where transportation is not
convenient or not operational, it is possible to contract a
third-party company or individual to perform the logistics
work [17]. In this more lucrative area, the third-party lo-
gistics market is now looming large and has even evolved
fourth- and fifth-party agencies and companies. e logistics
business has not only expanded geographically but also in
some of the better value-added industries. Once the user and
the agent have signed a good legal contract, the agent can
undertake all the services requested by the user and do the
heavy lifting of transporting goods for its users. When a user
makes an online purchase, he or she first receives infor-
mation about the product and then decides who to buy after
browsing a number of products. en the user needs enough
money to make the purchase, and finally, the logistics are
needed to deliver the purchased goods to the user. In this
complete business purchase behavior chain can be seen, the
user and the merchant to complete the entire transaction
process cannot be separated from information flow, capital
flow, and logistics, and logistics is the basis to ensure the last
link, if there is no logistics, capital flow and information flow
will have no meaning, the transaction is also impossible.
Logistics in the Internet of ings is an upgrade on the basis
of e-commerce logistics. It uses signal transmitters, tracking
chips, and signal receivers to realize the function of real-time
inquiry and monitoring during the whole process of goods
transportation. e IoT infrastructure should be able to
connect all possible assets, such as goods, containers,
transport vehicles, sensors, and employees, to ensure that the
entire logistics system can be tracked and managed as a
single Symphony unit.
2.3. Internet and Wireless Communication Technology. In
1969 in the United States, the Internet began with the APA
network, which refers to computer networks and the vast
network system linked by these computer networks. e
initial form consisted mainly of Web applications, and with
the rapid development of computer technology, the Web
entered the Web 2.0 era in the early 21
st
century. e initial
goal of the creation of the Internet was to make it possible for
humans to receive the information and content they need at
any time and place through this huge system of global access
and for everyone to chat and communicate with each other,
greatly bringing people closer to each other [18]. After
decades of development, Web 2.0 has become increasingly
mature, although the current network environment has
largely deviated from the inventor’s original intention of
open source and openness, it does not prevent relevant
practitioners and researchers from thinking about Web 3.0.
Some scholars believe that Web 3.0 will completely combine
reality and virtual, forming a seamless new world. Some
scholars also believe that there will be more emphasis on
mobility and that there will be more extended applications to
make mobile smart devices more popular and compre-
hensive. Another part of experts believes that cloud com-
puting will be the core of the next phase of development. All
three views above show that no matter how Web 3.0 de-
velops, it lays an unshakable foundation for the development
of IoT. IoT is the comprehensive purpose of the three, ev-
erything is connected to the Internet, and rely on wireless
communication technology to make connected devices with
more mobile attributes, rely on cloud computing to achieve
more and more powerful functions of APP, and finally
realize the beautiful technological vision of IoT changing life.
Picturing the description above gives a brief structural
diagram of the Internet of ings, which is shown in
Figure 1.
3. Agriculture in the Internet of Things
e great Adam Smith, in his 1776 book “”e Wealth of
Nations,”” said that the growth and increase of cities would
fundamentally change the pattern of agriculture. is will
provide a great market for agriculture, which can promote
the development of agriculture in the direction of mod-
ernization and efficiency, making the countryside more
efficient and organized, breaking through the constraints of
geographical limitations and traditional relationships. e
application of IoT to agricultural production is a new trend
in the development of agriculture in recent years. e global
arable land area accounts for about 35.9% of the Earth’s land
area, and the vast majority of countries still use the tradi-
tional farming model, which results in a low yield per square
meter of the land area [19]. e current applications of IoT
technologies in agriculture are mainly focused on remote
sensing data detection, cloud computing crop cycles, and
automated farming tools. e application of these tech-
nologies can greatly increase the dependence of agriculture
on weather and minimize the impact of natural disasters on
the harvest. Only when agriculture is developed can more
financial energy be invested in high-tech industries, and the
progress of high-tech industries will increase the yield of
agriculture, ultimately reaching a positive virtuous circle.
erefore, the detection of crop growth, soil water and
Mathematical Problems in Engineering 3
fertilizer content, pest and disease news, the establishment of
a nationwide spatial information system, digital agricultural
data, and the use of computer algorithms for seeding
planning should be actively applied to agriculture. Changing
the crude agricultural business model, saving human re-
sources, and creating high-yield green agriculture based on
ensuring the quality and safety of agricultural products are
the new guiding directions of IoT technology for agriculture
[20]. In conclusion, the topic of this study is scientific and
has practical social value and significance.
4. Improved Multiobjective
Evolutionary Algorithm
4.1. Multiobjective Optimization Problem. Many problems
encountered in agriculture can be abstracted as multi-
objective evolutionary problems, and this study converts the
multiobjective evolutionary problem into a mathematical
form without loss of generality.
min f(x) f1(x), f2(x),· · · , fm(x)
􏼁T, x Ω⊆Rn,(1)
where xis a n-dimensional dynamic decision vector in the
decision space, fis a m-dimensional dynamic target vector
in the decision space, and the formula f(x)represents the
mapping of the decision vector in the target space, which can
be represented by Figure 2.
Two dynamic decision vectors xand yare selected,
where xPareto dominates y. Define xas the Pareto op-
timal solution in the dynamic decision vector PS , and PF as
the dynamic frontier surface of Pareto, both of which are
defined as follows:
PS x Ω|xParetoBest
{ },
PF f(x)Rm|xPS
􏼈 􏼉.(2)
en choose zas the ideal point, which takes the
following range of values:
zz1, z2,. . . , zm
􏼁T, i 1,2,. . . , m
{ }.(3)
Corresponding to the ideal point is the closest zna , which
takes the following range of values:
zna z1
na, z2
na,. . . , zm
na
􏼁T, i 1,2,. . . , m
{ }.(4)
To address the basic features of the diversity problem, the
MOPs algorithm requires a set of dynamic decision vectors
to be selected and then a set of optimal dynamic objective
vectors to be computed. is set of objective vectors needs to
exhibit good convergence and diversity, which requires it to
be in the objective space, converge infinitely to the frontier
surface of Pareto and be distributed as homogeneously as
possible in the space.
4.2. Multiobjective Flexible Agricultural Scheduling Problem.
When scheduling agricultural machinery operations, there
are two major subproblems, the path subproblem and the
ranking subproblem. e former refers to the assignment of
a number of operations to the appropriate crop machines,
and the latter refers to the priority of the processing of a
number of operations assigned to each crop machine. e
three most important objectives in the agricultural sched-
uling problem are selected as total completion time, total
load, and critical load, which are defined by the following
equations:
Cmax max Ci|i1,2,. . . , n
􏼈 􏼉,
WT􏽘
m
k1
Wk,
Wmax max Wk|k1,2,. . . , m
􏼈 􏼉.
(5)
In the model building of multiobjective evolutionary
algorithms, the determination of dynamic change operators
is the focus of this study. An excellent dynamic change
operator can make an organic balance of extensive and
focused search in the decision space. e current algorithms
that can be chosen for effective dynamic change operators in
the field of computer algorithms are distribution estimation
algorithm, differential evolution algorithm, and goal genetic
algorithm. e objective genetic algorithm has better
adaptability compared with the former two, so it is selected
in this study to improve and optimize the multiobjective
evolutionary algorithm. e main operation step can be
summarized as selecting half of the best individuals in a
population of many individuals as the next generation, and
Electronic
tags Reader
IOT middleware IOT name resoultion IOT information
release
RFID
URI
RFID
item information
Figure 1: A brief basic structure diagram of the Internet of ings.
4Mathematical Problems in Engineering
then looping this step until the target individuals are found
to achieve a balance between convergence and diversity in
the target space.
Figure 3 illustrates the fitness assignment used by the
modified multiobjective evolutionary algorithm using the
goal genetic algorithm and the NSGA-2 algorithm. In the left
figure, it is assumed that all nondominated dynamic indi-
viduals have a fitness of 1, then the fitness of other domi-
nated dynamic individuals is added by 1. Using the fitness
sharing mechanism, the same fitness individuals can be
selected quickly. And in NSGA-2, we can first disregard the
nondominated dynamic individuals and set the fitness of the
remaining dominated dynamic individuals to 2, and then
iterate continuously so that all individuals have fitness
values. en they are distinguished using the crowding
distance degree.
After improving the algorithm, the multiobjective
problem generator is confirmed. First, the problem is op-
timally generated for an mobjective by splitting the com-
plete problem into two major parts and then subdividing it
into both parts. It is expressed as the following system of
equations:
min f1x1
􏼁
min f1x1
􏼁
· · ·
minfm1xm1
􏼁
minfm1xm1
􏼁
min fm(x) g xm
􏼁h f1x1
􏼁, f2x2
􏼁,. . . , fm1xm1
􏼁, g xm
􏼁 􏼁.
􏼨
(6)
Further classification is based on whether the position of
individuals in the population is the same in the dimension in
which the Pareto frontal plane is located. All problems with a
Pareto front surface in the horizontal dimension of 1 are
classified as normalized problems, and those with a Pareto
front surface in the horizontal dimension of 0.5 are classified
as non-normalized problems, which can be graphically
represented as Figure 4.
4.3. Improving the Performance of Multiobjective Evolutionary
Algorithms. In this study, different metrics were also se-
lected to test the performance of the improved
0MOGAf
1
3
6
1
4
8
f2
(a)
0 NSGA-2 f1
3
2
1
f2
(b)
Figure 3: Schematic diagram of fitness distribution of population individuals in MOGA and NSGA-2.
Decision space
X1
X2
f1
f3
f2
Target space
Figure 2: Mapping from decision space to objective space in multiobjective evolutionary algorithms.
Mathematical Problems in Engineering 5
multiobjective evolutionary algorithm. Since there are more
than ten metrics that can be tested on it, but all of them can
only test convergence and diversity singularly, it is necessary
to select four metrics, two of which test convergence and the
other two test diversity.
e first is the target generation distance metric to verify
whether the convergence of the algorithm is good. e test
formula is shown as follows:
GD(A) 1
|A|􏽘
xA
min d(x, y).(7)
Here, Arepresents the set of dynamic approximate solutions
of the Pareto front surface, and d(x, y)represents the Euclid
distance of xand yin the decision space. e smaller the
value obtained, the better the convergence of the algorithm.
In contrast to the target generation distance metric, the
target inverse generation distance metric does not simply
reverse the direction of the above equation but takes the
uniformly distributed individuals in the set of dynamic
approximation solutions of the Pareto front surface for
calculation. erefore, it does not measure the convergence
of the algorithm but characterizes the diversity of the al-
gorithm. e formula for the indicator is as follows:
IGD(A, P ) 1
|P|􏽘
xP
min d(x, y),
P􏽐T
i1Pi
T(i1,2,. . . , T).
(8)
As in (7), d(x, y)also represents the Euclid distance
between xand yin the decision space. When |P|is
sufficiently large, it approaches Pareto’s frontier surface
indefinitely, at which point the indicator can even charac-
terize the convergence of the algorithm. When the value
obtained from the calculation is smaller, it indicates better
convergence. And when the value of |P|is small, the
distribution of sampled individuals is more uniform, and the
diversity of the algorithm is better at this time.
e grid comparison metric is a diversity test metric
specially developed for the case of high-dimensional mul-
tiple objectives. First, we take an approximate solution set of
Pareto in the decision space, then divide the target space into
several grids, and put the dynamic target individuals in this
approximate solution set into the corresponding grids
according to the classification, then we can get the corre-
sponding values of the grids as follows:
DCI Ai
􏼁1
s􏽘
s
j1
CD Ai, hj
􏼐 􏼑.(9)
It is important to note that the grid comparison metric is
a comparative value of the diversity of individuals within
multiple grids, meaning that it is a relative value that is only
meaningful when comparisons are made.
e dynamic hypervolume metric is a relatively new
characterization parameter, and unlike the previous one that
takes an approximate solution set, it is selected as a reference
point in the target space.
HV(A, r) volume f1, r1
􏼂 􏼃×f2, r2
􏼂 􏼃×...×fm, rm
􏼂 􏼃 􏼁.
(10)
Combined with the following schematic, it can be seen
that the selected reference point is dominated by the solution
set, and then the result of the calculation is the decision space
volume of the solution set bounded by the reference point.
is spatial volume is also known as the super volume, and
its larger value means better diversity, i.e., better perfor-
mance of the algorithm, as shown in Figure 5.
5. Application of Algorithms in Agriculture
5.1. Monitoring of the Environment Using Algorithms.
Based on the equations obtained in the above study, the data
from the most fundamental arable land in agriculture, with
the same environmental variables, were collected from the
embedded sensor data collection device and the external
f2
f1
0
1
1
Normalization problem
Pareto Frontier
(a)
f2
f1
0 0.5
1
non-normalization problem
Pareto Frontier
(b)
Figure 4: Schematic diagrams of the Pareto fronts for the normalized problem (a) and the non-normalized problem (b).
6Mathematical Problems in Engineering
sensor data collection device, respectively, and the energy
changes of the two sensors were plotted over a certain period
of time and with the change of time.
As can be seen from Figure 6, the embedded multi-
channel sensor collection device measured in the same
tillage environment has a more fixed data collection period
compared to the external sensors. And it can always be
stabilized at the maximum energy output and will not go
below a predetermined threshold. So, the embedded mul-
tichannel sensor has better stability and this sensor is
selected for this study to monitor parameters such as soil
water content and oxygen content.
is study also compared the accuracy of data trans-
mission for irrigation water around the farm using an
embedded device and an external device with multiple data
transmissions. e same amount of data from the embedded
sensor data collection device and the external sensor data
collection device were transmitted 10 to 70 times, again
under the same environmental variables, and the integrity of
the data transmitted every 10 times was collected. e
Non-dominated
point
f2
0 f1
Reference
point
Figure 5: Schematic diagram of the hypervolume index of the target space.
Time
Maximum energy
Embedded sensor
Energy
Charge threshold
(a)
Time
Maximum energy
External sensor
Energy
Charge threshold
(b)
Figure 6: Comparison of energy stability between embedded sensors (a) and external sensors (b).
Mathematical Problems in Engineering 7
variation in the integrity of the data transmitted by the two
sensors over 70 times is plotted in Figure 7.
As can be clearly seen from the above graph, 70 repe-
titions of the same data transmission were performed in the
same environment. e measured final data integrity rate of
the embedded multichannel sensor is 68.8%, whereas the
final data integrity rate of the external sensor is only 62.5%,
which can be concluded that the signal transmission ac-
curacy of the embedded multichannel sensor data acquisi-
tion device is higher.
Modern agriculture usually consists of a small number of
farmers using advanced technology to cultivate a large area
of land, so the transmission of information between each
other is particularly important. In this study, five groups of
signal transmission frequencies, 200 MHz, 210 MHz,
220 MHz, 230 MHz, and 240 MHz, were selected for cal-
culation, and the results are shown in the following bar chart.
Figure 8 shows the reception ratio at different signal
frequencies. It can be seen from Figure 8 that when the used
ultrashortwave frequency is 230 MHz, the signal reception
ratio reaches the highest 95.63% in this group, so the
ultrashortwave with the frequency of 230 MHz is mainly
used for communication between farmers, weather infor-
mation, and transmission of important information such as
water level of nearby reservoirs.
e most important thing that needs to be provided by
IoT in agriculture is security risk assessment. e con-
struction of security risk standards is influenced by various
factors in the environment, and developed countries have
started this research earlier, so there are already some
Figure 7: Data integrity comparison between embedded and external sensors for irrigation water.
200 210 220 230 240
Signal frequency (MHz)
20
40
60
VSWR (%)
80
100
Figure 8: Reception ratio at different signal frequencies.
8Mathematical Problems in Engineering
research results. e research in this paper cannot be
completely copied because of the different environments at
home and abroad, but the data from foreign countries have
some reference value. With reference to foreign research
results and the concept of “”human-centered,”” we can use
the improved multiobjective evolutionary algorithm to
obtain the following linear graphs for the United States, the
United Kingdom, the Netherlands, and Denmark. Intoler-
able level and measures need to be taken to reduce the risk.
In terms of results given in Figure 9, it is important to
draw on the data already available in developed countries
and to use the Internet of ings flexibly and rationally to
reduce agricultural losses from natural disasters and other
causes to an acceptable level.
6. Conclusion
Nowadays, the Internet of ings (IoT) technology is be-
coming more and more mature, the Internet industry uses it
as a new tool to revolutionize the generation, and the ed-
ucation industry uses it as a prop to assist teaching, while its
development is not perfect in agriculture where people’s
food is the mainstay. is study analyzes and processes the
data of IoTapplications in agriculture based on an improved
multiobjective algorithm and establishes the relevant algo-
rithm model. is study first introduces the definition of IoT
technology, and then briefly describes its development and
future prospects. After introducing its advantages, a detailed
analysis of its application in agriculture is also presented, and
it is concluded that the development and construction of IoT
should be given first priority for the improvement of ag-
ricultural production. After that, its components are elab-
orated, and it is determined that information flow, capital
flow, modern e-commerce logistics, and Internet are the
fundamental components that can be generated and de-
veloped. e multiobjective evolutionary algorithm is im-
proved by introducing the advanced genetic algorithm, and
finally, an improved multiobjective evolutionary algorithm
model with good convergence and diversity is established.
After completing the optimization of the algorithm, the
embedded multichannel sensor acquisition device measured
in this experiment under the same tillage environment has a
more stable acquisition data cycle compared to the external
sensor. And it can be stable at the maximum energy output
all the time and will not go below the predetermined
threshold. e embedded multichannel sensor has better
stability, so this sensor was selected for this study to monitor
parameters such as soil moisture content and oxygen con-
tent. e same data transmission was repeated 70 times in
the same cultivation environment. e final data integrity
rate of the embedded multichannel sensor was 68.8%, while
the final data integrity rate of the external sensor was only
62.5%, leading to the conclusion that the embedded mul-
tichannel sensor data acquisition device is more accurate in
signal transmission. e IoTrequires timely communication
and consultation among users, and the signal reception rate
reached the highest 95.63% in this group when the used
ultrashortwave frequency was 230 MHz, so the ultra-
shortwave with the frequency of 230 MHz was mainly used
for communication among farmers, weather information,
and transmission of important information such as water
level of nearby reservoirs. Although certain results have been
achieved in this study, the agricultural industry is a rather
large and complicated system, and there are many more
points that can be studied and deepened. Moreover, IoT is a
rapidly changing technology, and it is important to use new
research results reasonably and timely in order not to be left
behind by the times. is paper has some limitations. Al-
though this research has made some achievements, the
agricultural industry is a very large and complex system.
erefore, it is necessary to establish a clear and stan-
dardized method and change the thinking mode of enter-
prises to make full use of the potential of the Internet of
things.
10
1.0E-11
1.0E-10
1.0E-9
1.0E-8
1.0E-7
1.0E-6
Accident Exceeds Probability
1.0E-5
1.0E-4
1.0E-3
1.0E-2
100
Agricultural losses/Dollar
1000 10000
America
England
Netherlands
Denmark
Figure 9: Relationship between foreign loss rate and agricultural property loss.
Mathematical Problems in Engineering 9
Data Availability
e data used to support the findings of this study are
available from the corresponding author upon request.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
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