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Volume 99
Studies in Big Data
Series Editor
Janusz Kacprzyk
Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Big Data” (SBD) publishes new developments and
advances in the various areas of Big Data- quickly and with a high
quality. The intent is to cover the theory, research, development, and
applications of Big Data, as embedded in the fields of engineering,
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coming from sensors or other physical instruments as well as
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transactions, such as emails or video click streams and other. The series
contains monographs, lecture notes and edited volumes in Big Data
spanning the areas of computational intelligence including neural
networks, evolutionary computation, soft computing, fuzzy systems, as
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Operations research, as well as self-organizing systems. Of particular
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More information about this series at https://link.springer.com/
bookseries/11970
Editors
Prasant Kumar Pattnaik, Raghvendra Kumar and Souvik Pal
Internet of Things and Analytics for

Agriculture, Volume 3
1st ed. 2022
Editors
Prasant Kumar Pattnaik
School of Computer Engineering, KIIT University, Bhubaneswar, India
Raghvendra Kumar
Department of Computer Science and Engineering, GIET University,
Gunupur, India
Souvik Pal
Department of Computer Science and Engineering, Global Institute of
Management and Technology, Nadia, West Bengal, India
ISSN 2197-6503 e-ISSN 2197-6511

Studies in Big Data
ISBN 978-981-16-6209-6 e-ISBN 978-981-16-6210-2
https://doi.org/10.1007/978-981-16-6210-2
© The Editor(s) (if applicable) and The Author(s), under exclusive

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Gateway East, Singapore 189721, Singapore
Preface
Edited book aims to bring together leading academicians, scientists,
and researchers to exchange and share their experiences and research
results on all aspects of Internet of Things analytics for agriculture. It
also provides a premier interdisciplinary platform to present and
discuss the most recent innovations, trends, and concerns as well as
practical challenges encountered and solutions adopted in the fields.
The book is organized into seventeen chapters.
Chapter “Functional Framework for IoT-Based Agricultural System”
proposed IoT-based framework which consists of a coherent
architectural viewpoint of the functional system. The proposed
functional framework is useful in organic farming, monitoring of water,
fertilizers, pesticides, crop yield and growth, etc. The framework
provides timely valuable analysis-based help to enhance the agriculture
production with quality of product and improve financial paradigm of
farmers.
Chapter “A Review on Advances in IoT-Based Technologies for Smart
Agricultural System” discusses the comprehensive review of available
IoT solution in the areas of the agriculture which is presented. Some of
the major targeted areas in the agriculture are selected, e.g., soil health
monitoring, crop health monitoring, IoT-based smart irrigation, and
real-time weather forecasting where automation can be implemented.
Chapter “Artificial Intelligence in Agri-Food Systems—An
Introduction” gives an insight of the fundamentals of machine learning
and deep learning with the emphasis on their application for AI
implementation in the field of agri-food material handling. Popular ML
algorithms, viz. support vector machine (SVM), K-nearest neighbor
(KNN), artificial neural networks (ANN), decision trees, and
convolutional neural networks (CNN), are discussed as feature
description methods for classification and recognition, based on the
product images.
Chapter “Intelligent Agro-Food Chain Supply” proposed IoT-based
supply management system that could significantly contribute to
improving the coordination of different strategic and operations units
because of its remote operating ability. A strong coordination system
among the different channels is the prime requirement of such a supply
chain management system (SCMS). Coordination among the different
channels can automatically improve the SCMS working efficiency in
terms of operational and strategic decisions and timely delivery of
goods on a priority basis.
Chapter “Machine Learning Approaches for Agro IoT Systems”
analyzed the sensed data and machine learning approaches used in
agro-IoT. Some of the machine learning algorithms are available for
predicting the solution to the agriculture problems. ML algorithms
learn from the given data and make the predictions precisely. A
combination of optimized CNN model with deep learning neural
network model provides promising results for IoT-based smart farming
system.
Chapter “AI-Based Yield Prediction and Smart Irrigation” gives an
insight about the current farming practices and several challenges that
the farmers are currently facing. It also projects how to bridge the gap
between these agricultural problems and challenges with the
emergence of the IoT and AI technologies to sustain precision
agriculture.
Chapter “IoT Enabled Technologies in Smart Farming and
Challenges for Adoption” focused on how smart farming is digitally
transforming using emerging technologies such as machine learning,
IoT, computer vision, and unmanned aerial vehicles. Several key
challenges for adopting smart farming are also discussed for providing
sufficient information before actual implementation.
Chapter “IoT Based Agricultural Business Model for Estimating Crop
Health Management to Reduce Farmer Distress Using SVM and Machine
Learning” deals with the above-said drawback by implementing an
agricultural business model which facilitates the farmers to know their
crop and the market better. The farmers can grow and cultivate crops
according to the market demands, thus leading to a good financial
prospect and lesser loss.
Chapter “Rice and Potato Yield Prediction Using Artificial
Intelligence Techniques” employed artificial intelligence (AI)
techniques for rice and potato crop yield prediction model in the region
of Tarakeswar block, Hooghly District, West Bengal, for rice and potato.
The major variables used were climatic factors, static soil parameters,
available soil nutrient, agricultural practice parameters, farm
mechanization, terrain distribution, and socioeconomic condition. The
analyzed datasets covered 2017–2018 seasons and were split into two
parts with seventy percent data used for model training and the
remaining thirty percent for validation.
Chapter “Socioeconomic Impact of IoT on Agriculture:A
Comparative Study on India and China” objective entails the emergence
of IoT and its widespread use has but provided for the evolution of
state policies and strategies in India and China. The study undertakes
comparative analysis, further utilizing mixed-method research, which
utilizes both quantitative and qualitative approaches. Additionally, the
chapter also deals with the huge impact of IoT on the development,
growth, and other socioeconomic parameters of farming communities
within India and China.
Chapter “The Impact of Irrigation on Generation of Marketable
Surplus in the Bolpur Subdivision, West Bengal” addressed that the
case study of Bolpur Subdivision is at the southeastern part of the
Birbhum District, West Bengal, India. Sources of irrigation of the study
area are canal, submersible pump, tank and river lift irrigation. After
consumption and keeping seed, farmers sold their excess production to
the market.
Chapter “A Farmer-Friendly Connected IoT Platform for Predicting
Crop Suitability Based on Farmland Assessment” proposed an
economically innovative machine learning Internet of Things platform
that can closely access and deliver the information pertaining to the
attributes of the environment and soil. The proposed system was tested
in real time at the local district of Karnataka state in India. The system
accurately predicted crop cultivation in relevance to kharif and rabi
seasons based on local environmental conditions gathered from the
data made available by the Indian Council of Agricultural Research and
also enhanced soil fertility by 33%.
Chapter “Smart Farming with IoT:A Case Study” discusses the brief
about the different areas in which IoT can be applied in the agricultural
field such as to know the weather conditions, to observe and monitor
the field, and to analyze the crop health, planting, spraying the
fertilizers, etc.
Chapter “Blockhain Solutions for Agro-Food Chain Systems” deals
with the rapidly spreading blockchain, the opportunities it offers will
be mentioned, and the studies using this technology in the field of agro-
food will be examined. This study will focus on the challenges faced in
this industry and the potential of blockchain technology in combination
with advanced information and communication technology and the
Internet of Things (IoT) devices to overcome these challenges.
Chapter “Efficiency and Reliability of IoT in Smart Agriculture”
discusses the improvement of ICT in different areas for agricultural
exploration. It is fundamental to increment the profitability of rural and
cultivating processes to improve yields and cost adequacy with new
innovation, for example, the Internet of Things (IoT). It can make rural
and cultivating industry measures more proficient by decreasing
human intercession through robotization.
Chapter “Architecture, Security Vulnerabilities, and the Proposed
Countermeasures in Agriculture-Internet-of-Things (AIoT) Systems”
aims to provide a survey of IoT systems, its enabling technologies, and
communication technologies. Moreover, we provide insights into IoT-
enabled agricultural applications along with its architecture and
research challenges. Finally, we discussed the security and privacy
issues that occur in agriculture IoT along with some cybersecurity
attacks.
Chapter “Protocols, Solutions, and Testbeds for Cyber-Attack
Prevention in Industrial SCADA Systems” investigated the security flaws
and most significant protocols that could nullify the security loopholes.
Further, the most suitable security recommendations are highlighted.
Finally, the pre-mortem testbeds of cybersecurity dedicated for SCADA
system have been clubbed together, which alerts the virtually
developed SCADA system about possible threats before it is actually
built on the real industrial ground.
We are sincerely thankful to Almighty to supporting and standing at
all times with us, whether it is good or tough times and given ways to
concede us. Starting from the call for chapters till the finalization of
chapters, all the editors have given their contributions amicably, which
is a positive sign of significant teamworks. The editors are sincerely
thankful to all the members of Springer especially Prof. Aninda Bose for
providing constructive inputs and allowing an opportunity to edit this
important book. We are equally thankful to a reviewer who hails from
different places in and around the globe shared their support and stand
firm toward the quality chapter submission.
Prasant Kumar Pattnaik
Raghvendra Kumar
Souvik Pal
Bhubaneswar, India
Gunupur, India
Nadia, India
Contents
Functional Framework for IoT-Based Agricultural System
Manoj Kumar Sharma, Rajveer Singh Shekhawat and Ruchika Mehta
A Review on Advances in IoT-Based Technologies for Smart
Agricultural System
Amit Kumar Pandey and Arpita Mukherjee
Artificial Intelligence in Agri-Food Systems—An Introduction
Ninja Begum and Manuj Kumar Hazarika
Intelligent Agro-Food Chain Supply
Manoj Kumar Sharma, Vijaypal Singh Dhaka and
Rajveer Singh Shekhawat
Machine Learning Approaches for Agro IoT Systems
C. R. Dhivyaa, S. Anbukkarasi and K. Saravanan
AI-Based Yield Prediction and Smart Irrigation
Immanuel Zion Ramdinthara, P. Shanthi Bala and A. S. Gowri
IoT Enabled Technologies in Smart Farming and Challenges for
Adoption
Rajesh Kumar, Deepak Sinwar, Amit Pandey, Tesfaye Tadele,
Vijander Singh and Ghanshyam Raghuwanshi
IoT Based Agricultural Business Model for Estimating Crop Health
Management to Reduce Farmer Distress Using SVM and Machine
Learning
Ishita Banerjee and P. Madhumathy
Rice and Potato Yield Prediction Using Artificial Intelligence
Techniques
Chiranjit Singha and Kishore C. Swain
Socioeconomic Impact of IoT on Agriculture:A Comparative Study
on India and China
Ramnath Reghunadhan and Ansel Elias Stanley
The Impact of Irrigation on Generation of Marketable Surplus in
the Bolpur Subdivision, West Bengal
Subhasis Mondal and Madhumita Mondal
A Farmer-Friendly Connected IoT Platform for Predicting Crop
Suitability Based on Farmland Assessment
Jason Elroy Martis, M. S. Sannidhan and K. B. Sudeepa
Smart Farming with IoT:A Case Study
Roopashree, Kanmani, Babitha and Pavanalaxmi
Blockhain Solutions for Agro-Food Chain Systems
Mustafa Tanrıverdi
Efficiency and Reliability of IoT in Smart Agriculture
Debabrata Dansana, Subhashree Sahoo and Brojo Kishore Mishra
Architecture, Security Vulnerabilities, and the Proposed
Countermeasures in Agriculture-Internet-of-Things (AIoT)
Systems
Nancy Kansal, Bharat Bhushan and Shubham Sharma
Protocols, Solutions, and Testbeds for Cyber-Attack Prevention in
Industrial SCADA Systems
Avinash Kumar, Bharat Bhushan, Ayasha Malik and
Raghvendra Kumar
Editors and Contributors
About the Editors
Prasant Kumar Pattnaik , Ph.D. (computer science), Fellow IETE,
and Senior Member IEEE, is Professor at the School of Computer
Engineering, KIIT Deemed University, Bhubaneswar. He has more than
a decade of teaching and research experience. He has published
numbers of research papers in peer-reviewed international journals
and conferences. He also published many edited book volumes in
Springer and IGI Global Publication. His areas of interest include mobile
computing, cloud computing, cyber security, intelligent systems, and
brain–computer interface. He is one of Associate Editors of Journal of
Intelligent and Fuzzy Systems, IOS Press, and Intelligent Systems Book
Series Editor of CRC Press, Taylor & Francis Group.
Raghvendra Kumar is working as Associate Professor in Computer

Science and Engineering Department at GIET University, India. He
received B.Tech., M.Tech., and Ph.D. in computer science and
engineering, India, and Postdoc Fellow from Institute of Information
Technology, Virtual Reality and Multimedia, Vietnam. He serves as
Series Editor of Internet of Everything (IOE): Security and Privacy
Paradigm, Green Engineering and Technology: Concepts and
Applications, published by CRC Press, Taylor & Francis Group, USA, and
Bio-Medical Engineering: Techniques and Applications, published by
Apple Academic Press, CRC Press, Taylor & Francis Group, USA. He also
serves as Acquisition Editor for computer science by Apple Academic
Press, CRC Press, Taylor & Francis Group, USA. He has published
number of research papers in international journal
(SCI/SCIE/ESCI/Scopus) and conferences including IEEE and Springer
as well as served as Organizing Chair (RICE-2019, 2020), Volume Editor
(RICE-2018), Keynote Speaker, Session Chair, Co-chair, Publicity Chair,
Publication Chair, Advisory Board, Technical Program Committee
Members in many international and national conferences and serves as
Guest Editors in many special issues from reputed journals (Indexed
By: Scopus, ESCI, SCI). He also published 13 chapters in edited book
published by IGI Global, Springer, and Elsevier. His research areas are
computer networks, data mining, cloud computing and secure
multiparty computations, theory of computer science and design of
algorithms. He authored and edited 23 computer science books in the
field of Internet of things, data mining, biomedical engineering, big
data, robotics in IGI Global Publication, USA; IOS Press Netherland;
Springer; Elsevier; and CRC Press, USA.
Souvik Pal , Ph.D., MCSI, MCSTA/ACM, USA; MIAENG, Hong Kong;

MIRED, USA; MACEEE, New Delhi; MIACSIT, Singapore; and MAASCIT,
USA, is working as Associate Professor in the Department of Computer
Science and Engineering at Global Institute of Management and
Technology, India. He has received his both master’s degree and
doctorate degree from KIIT University, Bhubaneswar, India. He has
published several research papers in peer-reviewed international
journals and conferences (Scopus and ESCI). He has authored a book on
computer science in the field of cloud computing. He was appointed in
many conferences as Session Chair, Reviewer, and Track Co-chair. He
also serves as Editorial and International Advisory Board Member for
many journals and conferences. His research area includes cloud
computing, big data, Internet of things, and data analytics.
Contributors
S. Anbukkarasi
Department of Computer Science and Engineering, Kongu Engineering
College, Erode, Tamil Nadu, India
Babitha
Sahyadri College of Engineering & Management (Affiliated to
Visvesvaraya Technological University, Belagavi, Karnataka, India),
Adyar, Mangaluru, Karnataka, India
P. Shanthi Bala
Pondicherry University, Kalapet, Puducherry, India
Ishita Banerjee
Dayananda Sagar Academy of Technology and Management, Bangalore,
India
Ninja Begum
Department of Food Engineering and Technology, Tezpur University,
Assam, India
Bharat Bhushan
School of Engineering and Technology, Sharda University, Greater
Noida, India
Debabrata Dansana
KISS University, Bhubaneswar, Odisha, India
Vijaypal Singh Dhaka

Manipal University Jaipur, Jaipur, India
C. R. Dhivyaa
Department of Computer Science and Engineering, Kongu Engineering
College, Erode, Tamil Nadu, India
A. S. Gowri
Manuj Kumar Hazarika

Department of Food Engineering and Technology, Tezpur University,
Assam, India
Kanmani
Nancy Kansal
Noida Institute of Engineering and Technology, Greater Noida, India
Avinash Kumar
School of Engineering and Technology, Sharda University, Greater
Noida, India
Raghvendra Kumar
GIET University, Gunupur, India
Rajesh Kumar
Department of Computer and Communication Engineering, Manipal
University Jaipur, Jaipur, India
P. Madhumathy
Dayananda Sagar Academy of Technology and Management, Bangalore,
India
Ayasha Malik
Noida Institute of Engineering Technology (NIET), Greater Noida, India
Jason Elroy Martis

Department of ISE, NMAM Institute of Technology, Udupi, India
Ruchika Mehta
Brojo Kishore Mishra

GIET University, Gunupur, Odisha, India
Madhumita Mondal
The West Bengal University of Health Sciences, Kolkata, India
Subhasis Mondal
Department of Geography, Seacom Skills University, Santiniketan,
Bolpur, West Bengal, India
Arpita Mukherjee
Academy of Scientific and Innovative Research, Durgapur, India
CSIR-Central Mechanical Engineering Research Institute, Durgapur,
India
Amit Pandey
Department of Computer Science, College of Informatics, Bule Hora
University, Bule Hora, Ethiopia
Amit Kumar Pandey

Academy of Scientific and Innovative Research, Durgapur, India
Pavanalaxmi
Ghanshyam Raghuwanshi
Immanuel Zion Ramdinthara

Ramnath Reghunadhan
Department of Humanities and Social Sciences, Indian Institute of
Technology Madras, Chennai, Tamil Nadu, India
Roopashree
Subhashree Sahoo
GIET University, Gunupur, Odisha, India
M. S. Sannidhan
Department of CSE, NMAM Institute of Technology, Udupi, India
K. Saravanan
Department of Computer Science and Engineering, Anna University
Regional Campus Tirunelveli, Tirunelveli, India
Manoj Kumar Sharma

Shubham Sharma
Lovely Professional University, Punjab, India

Vijander Singh
Department of Computer Science and Engineering, Manipal University
Jaipur, Jaipur, India
Chiranjit Singha
Department of Agricultural Engineering, Institute of Agriculture,
Sriniketan, West Bengal, India
Deepak Sinwar
Ansel Elias Stanley

Centre for East Asian Studies, Jawaharlal Nehru University, New Delhi,
India
K. B. Sudeepa
Department of CSE, NMAM Institute of Technology, Udupi, India
Kishore C. Swain
Department of Agricultural Engineering, Institute of Agriculture,
Sriniketan, West Bengal, India
Tesfaye Tadele
Department of Information Technology, College of Informatics, Bule
Hora University, Bule Hora, Ethiopia
Mustafa Tanrıverdi
Gazi University, Ankara, Turkey
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
P. K. Pattnaik et al. (eds.), Internet of Things and Analytics for Agriculture, Volume 3,
Studies in Big Data 99
https://doi.org/10.1007/978-981-16-6210-2_1
Functional Framework for IoT-Based

Agricultural System
Manoj Kumar Sharma1 , Rajveer Singh Shekhawat1 and
Ruchika Mehta1
(1) Manipal University Jaipur, Jaipur, India

Email: rajveersingh.shekhawat@jaipur.manipal.edu
Ruchika Mehta
Email: ruchika.mehta@jaipur.manipal.edu
Abstract
In the present age of agriculture, governments are trying to increase
yield production along with the improved financial health of the
farmers. In continuation of the same, a visible technological
advancement has taken place in agriculture. Internet of things (IoT) is
one of the most revolutionary technologies which have been the game-
changer in smart cities, transportation and its monitoring, new-age
vehicles, healthcare, industry, security and agriculture, etc. IoT
technology is mainly based on four factors: (1) high performance with
reduced power requirement. (2) Sensors used to collect real-time data
which stored on cloud-based storage for further analysis. (3) Big-data
tools play very crucial role in analysis of captured data which is in
different forms. (4) High-speed Internet connection plays very
important role in IoT architectures. IoT has been the game-changer in
the agriculture sector and is being used for various purposes (i.e. yield
analysis, monitoring of water, soil, crop, etc., analyse the requirement of
fertilizers and other gradients, pesticides requirement and many more).
This chapter is presenting an IoT-based architecture modelling for the
monitoring of various aspects related to agriculture (i.e. water
requirements, soil health analysis, fertilizers and pesticides
requirement) and a GPS-based monitoring system is also introduced.
However, data will be collected from different sensor(s) and store on
the cloud for further analysis. The proposed IoT-based framework
consists of a coherent architectural viewpoint of the functional system.
The proposed functional framework is useful in organic farming,
monitoring of water, fertilizers, pesticides, crop yield and growth, etc.
The framework provides timely valuable analysis-based help to
enhance the agriculture production with quality of product and
improve financial paradigm of farmers.
Keywords IoT – Food chain – Sensors – Wireless – Agro-products –

Machine learning
1 Introduction
Food is one of the basic necessities of life. Sixty percent of population in
the entire world depends on farming for their food requirements. It is
becoming very difficult to manage the food of the world population
which is increasing rapidly. Because even today, most farmers give
priority to traditional farming. Due to traditional practices in
agriculture neither improvement in the quality of agricultural products
nor production can be increased and similarly traditional practices in
agriculture are responsible to an increasing shortage of food, as well as
the economic condition of the farmer. In these days, the rapid growth of
technology is increasing. The quality of the products as well as the
production capacity can also be increased by increasing the use of new
technology. As a result of which, along with the quality of food grains,
economic growth of farmers can also be guaranteed. Also, the
availability of food for everyone can also be ensured. The Internet of
things (IoT) is a logical network of physical objects and devices.
However, it has the information exchange capability without having any
human intervention. IoT has been the integral part of all kinds of
automations and drastically enhanced the productivity of the
businesses. IoT network consists of sensors, robots, big-data tools for
data analytics, machine learning and deep learning algorithms for data
analysis and prediction, drones, some other electronic circuits, cloud
storage, and processing mechanism, wireless networks, radio frequency
identification (RFID), GPS, etc. IoT is one of the prominent technologies
to enhance the agro-products quantitatively and qualitatively. The
urbanization is increasing at the rapid speed and demand of agro-
products is also rapidly increasing but the availability of labour is
substantially decreasing for the agriculture operations. The IoT-
controlled robots known as agribots are in high demand in the
agriculture. They trained about the surroundings with the help of
sensor and artificial intelligence technologies. Agribots are not
substituting the labour cost but also increasing the yield productions
and minimizing the wastage. The weeding robots are very helpful to
identify the weeds in the crops with the help of image processing
techniques and after identification easily weed out with the help of
robotic arms. As we know, the excess use of the pesticides turning the
fertile lands to the barren land and degrade the quality of the products
which directly affect the human health. In this situation, removal of
weeds with the help of agribots is not only saving health of the soil and
crop but also saves the farmers hard money. GPS technology with the
IoT control also helps the farmers to control their heavy farming
equipment and machines like tractors to automatically ploughing the
field from the remote place with high accuracy and the work progress
also can be monitored through the mobile applications. Here, machine
learning techniques also help the farmers to make their machines
smarter. IoT-controlled agribots with the help of machine learning and
image processing tools help the farmers in automatic harvesting of the
crops (i.e. picking vegetables, fruits, etc.). Image processing and
machine learning tools do not only help in the picking the fruits but
also help in the identification of the ripe fruits and unripe fruits.
Argibots not only help in the harvesting, ploughing and labour
substitution but also help in the movement of heavy material, product,
measuring the spacing between the plants at high accuracy, etc. IoT can
control the drones also which can be equipped with the cameras and
sensors, spraying chemical canes. With the help of such drones, farmer
can monitor the growth of the crop, disease, pests and can
automatically spray the pesticides in the specific area only, and this
whole exercise can be done and monitored remotely; farmer needs not
to come actually in the farms.
Another IoT-controlled technology which is very helpful and game-
changing for the farmers is remote sensing. Sensors can be mounted in
the different sectors of the field for different purposes (i.e. moisture
sensors, heat sensors, health monitoring, pests’ sensors, etc.) and data
collected by the sensors stores on the cloud-based storage and big-data
and machine learning tools uses analyse this high volume and versatile
data for useful insights. Accordingly, farmer can monitor the crop in
different stage and can take corrective actions. The monitoring with the
help of remote sensing in agricultures is mainly for crop, weather, soil
quality, irrigation and harvesting. An IoT-enabled agriculture
framework is given in Fig. 1.
Fig. 1 IoT-enabled agriculture framework
2 Scope of IoT in Agriculture

IoT is the new era of the future information communication
technologies (ICT). IoT is a networking of devices (i.e. sensors, cameras,
electronic devices, etc.). In an IoT network, devices are programmed to
work together and crease an autonomous monitoring and control
environment. Nowadays, IoT has been integral part of our daily life
(controlling of TVs, air conditioner, refrigerator, washing machine,
electronic doors/locks, etc.). An IoT network is mainly depends on five
components (i.e. sensors/electronic devices, cloud server, Internet, LAN
and software application). Initially, IoT device(s) connects through the
LAN and transmits data through cloud server, and from there, data
reaches to software application through two-way communication.
(a) IoT Devices—are intelligent devices having wireless sensors and
transmit data over the cloud server.
(b) LAN—network is used to capture data from the sensors and other
devices.
(c) Internet—is helpful in the communication of servers and

software applications.
(d) Back End Services—consist of end-user access, control, remote

server(s), mobile apps. This service helps in the information
exchange in between user application and devices.
IoT has a substantial impact on new-age agriculture technologies,

monitoring, yield production, storage, transport and distribution, and it
can moderate the production cost and minimize wastage of resources,
agro-products and maximize the productivity of the farms. IoT is a hi-
tech capital-intensive technology which helps the farmers to grow
sustainable food with low cost and quality. In agriculture, IoT can help
the farmers in many ways. Most importantly, it helps the farmers in (1)
high-quality production with low cost. (2) It helps the farmers for
storage, transportation and distribution of agro-products to consumer
in time, with low cost and high gain.
2.1 Irrigation and Fertilizers

Right time irrigation of crops is very important for the health of the
crop and qualitative and quantitative production of the yield. All crops
and vegetations absorbs water content from the land water source or
rainwater. However, surface water levels rest in deeper side of the earth
and rainwater is available only for few months of the year, which
demands for alternative irrigation methods. An alternative irrigation
sources can be pounds, rivers and ground water. Each crop has its own
life cycle, so the amount of water and number of times is varying and
depends on some other factors (i.e. soil type, crop time, climate, amount
of rainfall, way of cultivation, sunlight, life cycle). In agriculture, lack of
information and knowledge about new tools and technologies is a
biggest obstacle in the growth of this sector. Most of the farmers adopts
agriculture practices as per their own understanding and knowledge
transferred from their predecessors. The nature of the soil and its
ingredients are not same through the globe. As per the region and
climate, its nature also changes. The use of the fertilizers in quality,
quality and type is also a challenging issue in the agriculture. In general,
farmers are using their own knowledge for the farming and assessment
of the fertilizer quantity and type is not possible without undersetting
the soil requirements. Here also, IoT is very helpful. IoT-based sensor
control mechanism can easily assess the nitrogen level of the soil,
chemical composition of the soil along with the assessment of crop life
cycle, type of the soil, its available ingredients and its actual
requirement of the fertilizers. Excess use of fertilizers is not only giving
financial burden to farmers but also destroying the soil health. An
appropriate use of the fertilizer not only reduces the production cost
but also helps to improve the soil and crop quality and health, which
leads the increasing yield production.
2.2 Disease Control

According to some studies, around 15–20% of the crop destroyed by
the pests and hundreds of pesticides are in use throughout the globe.
The early detection of the pest can save this wastage of grains and can
control the amount of pesticides used in the crops. One of the
important mechanisms for early detection of the pests is image
processing. Deep learning algorithms are capable enough to detect and
prevent diseases and pests in the plants at their early age. The IoT-
based machine learning solution can be provided to the farmers to
remotely assess the amount of pesticides to be sprayed in the field to
remove the present pest and minimize the chances of future pest
attacks. AI-based algorithms can easily identify the faulty areas of the
field and suitable pesticide and adequate amount can be sprayed in the
target sector of the field. RADAR-based drones are very suitable
solution to this problem because in the drawn we can have image
processing units also which can assess the pests in the crop and can
estimate adequate amount of pesticides to be sprayed in real time.
RADAR-based drones are already having the longitudinal and
latitudinal data of the field. This information helps the drone to target
only the specified area. With the help of IoT-controlled application, we
can assess the nitrogen, potassium and phosphorus value in the plants
to monitor the plant health. Uncontrolled use of the pesticides has been
a threat to the human health and has been cause of several critical
illnesses. There is an international code of conduct which allows the
judicious use of pesticides in crops. There are certain guidelines to
assess the usages of pesticide in agriculture and food storage. The food
safety and standard and health and family welfare of different
governments are working over the standardization of the use of
pesticides in the crops and foods. The geographical location of the
cultivated land greatly affects food and supply chain logistics. The
controlled use of the pesticides in the agriculture crops is mandatory to
increase the fertility of the soil and quality of the crop which can save
the bioorganisms lives in the field and is very useful for the crops also,
and at the same time, it can prevent the harmful effects on human body.
Nowadays, organic agriculture is another famous term which is
frequently spreading in the agriculture business. It is the only
mechanism which can sustain the health of crop, people, soil and
ecosystem. In this, the use of synthetic pesticides is strictly prohibited.
It develops useful agronomic practices like intercropping [1].
2.3 Precision Farming

Internet of things (IoT) is an appropriate and mostly usable technology
in agriculture which transforms the traditional era of farming in to new
era of precision farming. It uses the electronic devices and sensors,
autonomous hardware and vehicles, robotics, etc. for the farm
management [2, 3]. Smart irrigation and management of water
resources is one of the key managements of precision agriculture.
Optimized utilization of the water can enhance the productivity of the
crop at low cost. Another key component of precision farming is
monitoring the use of minimal fertilizers, identification of pest and use
of pesticides. Monitoring is one of the active aspects in precision
farming, and it can be done with the help of sensors and unmanned
aerial vehicles. Precision farming is classifiable data collection, analysis,
evaluation and implementation applications [2]. Use of multiview UAV
systems in the agriculture monitoring can have a breakthrough in the
agriculture. UAV can capture coloured imaged from different part of the
field and later on these images can be analysed by the algorithmic
systems and outcomes can help the farmers to take appropriate
decision for irrigation as well as crop health. Nowadays, smart sensors
are also integrated with the UAVs which helps mapping of grain volume
during forage harvesting. With the help of IoT, UAV and sensor
technology, the bottleneck in the breeding programs in farming can be
mitigated in precision phenotyping and farming.
2.4 Crop Yield

IoT helps the farmers in growing quality agro-products with minimum
cost. For the same, IoT can be used in the monitoring of the soil, crops,
pesticides, temperature, light, moisture, humidity, etc. with the help of
different sensors. It can help in assessment of irrigation requirement
and its automation. With the help of IoT, physical assessment of the
field conditions is not mandatory; farmers can monitor their field
conditions remotely which is much comfortable to the farmers as
compared to traditional agriculture. IoT has been the driving force
behind the increasing agro-product production with quality and at low
cost. The market cap of IoT devices and services in the market will be
increasing up to 225 million dollars by 2024. Smart farming
(application of ICT to agriculture) will enable growers and farmers to
control every aspect of the production cycle reducing waste and
enhancing productivity. There are certain IoT-based smart farming
frameworks that are helping to revolutionize agro-production.
2.5 Storage and Transportation

According to different research reports, 25–30% of the agro-products
are in wastage due to inappropriate cold storage and supply chain.
Thus, demand of the fresh agriculture products has substantially
increased. However, traditional transportation and distribution system
is not capable enough to supply fresh agro-products to consumers in
time. However, with the help of IoT technology, we can achieve fresh
food supply to consumers, intelligent production of fresh agro-
products, monitoring the cold storage and supply logistics in storage
and transmission [4].
2.6 Livestock Monitoring

Livestock monitoring is the monitoring of cattle behaviour. IoT-enabled
systems help the farmers to monitor the cattle behaviour and other
activities like milking time, amount, speed, amount of food needed and
consumed by the cow, how and how much it walks, is there any change
in walking and eating pattern, etc. In livestock monitoring ambient
sensors are used to estimate the moisture, stress, temperature, and
hazardous gas sensors are used to estimate the amount of
formaldehyde, ammonia, hydrogen sulphide, methane [5]. A real-time
monitoring of the livestock health (temperature, heart rumination,
rhythm, etc.) is done with the help of ZigBee and BLE like cattle sensors
[5].
2.7 Farm Machinery and Maintenance

Agriculture machinery can be remotely controlled, and their
maintenance is also can be done remotely. The IoT-controlled robots
knows as agribots are in high demand in the agriculture. They trained
about the surroundings with the help of sensor and artificial
intelligence technologies. Agribots are not substituting the labour cost
but also increasing the yield productions and minimizing the wastage
as explained in Section 1 introduction and in [6].
3 IoT Framework for Smart Agriculture

As we know, IoT is a logical network of physical objects and
applications. In agriculture, IoT implements through the sensors,
imaging tools, robots, data analytical tools like big-data, machine
learning, drones, GPS, etc. Initially, farmer has to invest for the
infrastructure and equipment’s like sensors, bots, drones, highly skilled
staff, electricity and Internet connectivity, installation and maintenance
cost of the equipment’s, efficient supply chain. A detailed standard IoT
framework for the agriculture is given in Fig. 2.
Fig. 2 Detailed IoT framework
3.1 Hardware Requirement

IoT-based agriculture applications are having a network of wireless
sensors, wireless network, Internet technologies, cloud-based storage,
GPS, relays, big-data and machine learning tools, etc.
3.1.1 Sensors
Sensor technology is the key component of the IoT-based applications.
In the agriculture, IoT-based solutions capture different type of data
related to the soil, fertilizer, crop growth and its health, pests,
environmental, irrigation requirements, temperature, harvesting
requirements and many more.
Some of the important sensors which used in the agriculture are
explained as:
(1) Airflow Sensor: This types of sensors are used to measure the
wind direction and its permeability.
(2) Optical Sensors: Optical sensors are used temperature to
predict clay, water content in the soil, organic matter in the soil.
(3) Electromagnetic Sensors: These sensors are used to measure

the organic matter, water content in the soil, salinity of soil, soil
texture, etc.
(4) Electrochemical Sensors: These sensors are used to measure

the pH and nutrients level of the soil.
(5) Acoustic Sensors: These sensors are helpful in the soil texture
formative.
(6) Pest and Pesticide Sensor: These are the sensors which used to
monitor the pest in the crop and amount of pesticide is used in
the crop.
(7) Water Leak Detector: These are the sensor which are very
useful in the precision farming irrigation.
(8) Rain Drop Sensors: These sensors are helpful in the measure
the amount of rain fail.
(9) Water-Level Sensors: These sensors are helpful to monitor the

water level of the ponds uses for the irrigation purpose.
(10) IPR Sensors: These sensors are used to monitor animal

intrusion.
(11) IR Sensors: These sensors are used to monitor the growth of the
plants with the help of energy wavelength measuring.
3.1.2 GPS Module

GPS-based IoT applications used in the precision farming for the
mapping, planning, soil health monitoring, yield mapping, crop
scouting, machinery control and guidance regardless the physical
presence of the farmer at the farm and odd environmental conditions
like high temperature, rain, dust cloud, darkness, fog, etc. A GPS module
uses obtained data from the sensors placed in different sectors of the
field and drones. This high volume data sends to the cloud-based
storage and monitoring system with the help of wireless sensor
network. GPS module allows the farmer for pinpoint mapping of the
pest, disease, weed in the crop. The collected data can be used with the
drones to spray the pesticides in the pinpoint sections of the field with
minimized chemical drift which ultimately benefited to the originality
of the crop yield and environment. With the help of such GPS modules,
the monitoring of the crop area is also possible by feeding the GPS map
in the drone [7].
3.2 Relays
Basically, relay is a AC/DC switch which used to operate different
electronic and electrical devices in the IoT-based agriculture
architecture. Relays use solid-state, electromagnet principles to
operate. They are mostly used when a low power signal is needed to
operation a circuit. A contactor is a type of relay which used to control
heavy motors and other electric devices. Electromechanical relays work
as circuit breakers to detect faults and overload in the electric supplies.
Relays are very useful in the radioactive safety-critical devices because
of its high resistivity than semiconductors [8].
3.3 Software and Algorithms

Machine learning, big-data tools have been key components of the IoT-
enabled agriculture applications. A wireless network is used to connect
all the devices. This is very useful network for the peer-to-peer
transmission of the data specially when Internet connectivity is not
consistent. Various big-data tools are used in agriculture for the
predictive insights of the agriculture operations (i.e. crop and yield
health monitoring through the temperature data analysis, attack or
possible attack of the pests in the field or in a specific area of the field
and quantitative requirement of the pesticides in a specific area,
irrigation requirement of the crop, etc.). Similarly, AI-enabled cameras,
sensors and other operational devices can easily filter the data and
other information in real time to take an appropriate decision in
agriculture field as well as inventory monitoring and handling. With the
help of machine learning algorithms, we can easily analyse different
time stamp requirement of the agriculture. The health monitoring of
the yield in the warehouses is very important and AI-enabled
applications can easily handle this precious operation. Safety-critical
operations also can monitor, controlled by the AI-enabled operations.
AI and big-data tools are very important to improve the efficiency,
accuracy, reduce production and other operational costs of the crop,
etc.
3.3.1 ML-Based Image Processing Algorithms

Over the decades, machine learning algorithms are significantly
contributing in agriculture. Prediction of the yield is one of the critical
issues in precision farming. Yield prediction is based on various factors
weather, soil, climate, seed quality and climate, etc. Long short-term
memory, convolutional neural network, deep neural networks are the
widely used deep learning solutions for agriculture products yield
prediction and monitoring of the crops [9]. Machine learning algorithm
can find the insight of the dataset and can predict the yield production.
Some of the deep learning algorithms which are used in agriculture are:
(1) Deep Neural Networks (DNN): In these algorithms, hidden
layers are varying; otherwise, it is similar to artificial neural
networks. In deep neural networks, most of the hidden layers are
fully connected [9].
(2) Convolutional Neural Network (CNN): As compared to other

networks, it has less number of learning parameters. CNN
network has convolutional layer which has feature maps and
filters, and feature maps are the filter output. Pooling layer used to
down sample the convolutional layer feature maps and minimize
overfitting and generalized the feature representation. Fully
connected is output layer in CNN [9].
(3) Long Short-Term Memory (LSTM): This network works well

with the sequential event prediction. So far, a number of LSTM
architectures are presented (stacked LSTM, Encoder-Decoder-
LSTM, CNN-LSTM, Vanilla-LSTM, Generative-LSTM and
Bidirectional-LSTM [9]. Thus, multilayer perceptron networks
have some limitations like temporal structure, stateless, fixed
sized input and output, messy scaling, etc. It is one kind of
recurrent neural network, and it resolves all the limitations of
multilayer perceptron networks.
(4) 3D-CNN: In this, CNN model kernels travel through the depth,
height and length and generate 3D activation map. The application
of this network is in surveillance systems, medical where we
capture video streaming [10].
(5) Faster R-CNN: It is a region-based CNN model and uses for object
detection. So far, we have different variants of this family like
faster, fast, mask convolutional network and R-CNN. A region
proposal network is invoked in faster R-CNN network to interpret
extended features to the [10].
(6) Multitask Learning (MLT): In this model, a representation is

shared among tasks to improve model performance. The
application areas of this network are natural language processing,
speech recognition and drug discovery, etc. [10].
(7) Deep Recurrent Q-Network (DQN): DQN is the variation of

reinforcement learning and deep learning. This algorithm is
frequently uses with crop yield prediction [10].
3.4 Data Storage

The use of cloud-based services (i.e. storage, processing) is an essential
port of any fruitful IoT-enabled agriculture application. In most of the
village and farms, an uninterrupted high Internet connectivity is a
myth. A peer-to-peer information exchange and decision are very
important part of IoT-controlled agriculture applications. Different
agriculture devices/sensors collect the required data and store it in the
cloud storage. So the stored data can be accessed/analysed as and
when required. A cloud-based data storage provides a kind of
portability of storage, easy and efficient analysis of the data and in
speculated time period. A big-data tools can find the insights of the data
stored in the web. The use of cloud-based data storage and
transmission provides a flexible, less time-consuming operations.
3.5 Methodology
As we know, IoT is a logical network of physical objects and
applications. IoT works over the real-time data capturing, information
exchanging, analysing and commanding, controlling to operations.
Different sensors are uses to capture the real-time data (temperature,
moisture, wind, irrigation requirement, crop health, yield production,
pests, etc.). These sensors collect the data in time series manner and
directly transmit to the local data storage through wireless network.
Finally, data stores on cloud-based storage. From the cloud-based
storage, different data analytic tools like big-data algorithms, machine
learning algorithms, etc. analyse the data to discover the real insights of
the data; on the basis of the insights, different operational activities are
initiated like if there is a requirement of pesticides, if yes in which area?
Only the required operation initiates in area-specific form. As per the
moisture data of the soil, system automatically assesses the irrigation
requirement of the crop and controls the water jets, motors in area-
specific manner. Similarly, the health of the crop is also monitored by
the IoT-based sensors and an auto-system takes required actions. With
the help of IoT-controlled agriculture application even months before
the harvesting of the crop, we can predict the possible yield production.
After yield production, its storage and movement are also track and
trace through the IoT-enabled sensors (i.e. GPS, temperature sensors,
moisture sensors, etc.). After storage of the yield, the assessment of
market demand and supply an automated IoT-controlled system can
synchronize the supply of the agriculture yield to the consumer [11].
3.5.1 Architecture of Smart Agriculture Systems

Smart agriculture system builds with the help of intelligent
technologies like IoT, big-data and machine learning algorithms for data
analytics, GPS remote sensing, cloud computing for storage and
analysis, sensors to capture data for different variables. There are two
main components of the smart architecture: (1) data and (2)
management.
In the farm architecture, various sensors are used to capture
continuous data for different variables. The data remotely stores in the
cloud storages and with the help of various data processing
applications like data mining, video and image processing machine
learning algorithms, decision support system, big-data tools, etc. The
monitoring and mapping of the activities done through the data
visualization tools where system monitors the environmental
conditions, yield, temperature and sunlight, soil, etc. Finally, in the
management, system manages the actuator, sensor, identification of the
tools and devices, agent monitoring, vehicle control like UAV, finance
services, etc. [12].
3.5.2 Sensor-Based Data Capturing

Different data variables responsible for the crop growth and production
are acquired through the different sensors. Sensors have been integral
part of the IoT-enabled agriculture operations. They can be mounted in
or over the ground, UAV, trees and some other appropriate places.
Sensors grid supports the remote sensing and monitoring of the
agriculture requirements and functions. Different biophysical
parameters of the agriculture can be assessing with the help of radar,
satellite-based sensors. UAVs are another era of development and
control of agriculture activities through IoT-controlled environment.
UAVs can capture the aerial view of the crop and different variables can
be captured with the help of GPS and cameras mounted on the UAV
device.
3.5.3 Data Preparation and Feature Extraction

Traditionally, we irrigate the crops on the basis of farmer’s knowledge
and decent use of water for irrigation has been a great problem.
Inefficient use of water for irrigation turns in less or more flooding of
water, which results in reduced growth of crop and reduced availability
of calcium to crop and crop rot and water wastage, respectively. IoT-
based intelligent irrigation systems estimate the climate conditions and
life cycle of the crop and then only finalize the irrigation plan. On the
basis of such analysis, pump motor automatically operates when soil
moisture reduced below welting point. In [13], Hargreaves et al. have
proposed the smart irrigation method extra-terrestrial radiation and
temperature. Different sensors, like heat sensor, moisture sensor,
cameras, etc. collect the data. This data reaches to the data centre,
where noises of the data filtered and applied with some smart
algorithms to train the models. These trained models pretend the need
of irrigation to the crops.
3.5.4 Classification and Quantification

Quantification of the feature is an important for the generation of
adequate feature set and to understand the underlying phenomena.
Thus, two machine learning features selection methods name RFs and
BoRTs can be directly uses for the feature selection along with the
feature quantification and classification of the data in different class
using confusion matrix, decision-tree, k-NN, etc. Such tools work in
both the forms, i.e. individual and sub-set evaluation [9].
4 Yield Prediction
Production of the agro-products is depending on various factors and
integral aspects. Water is an integral aspect of the agriculture which
directly affects the yield production. In the IoT system, different
sensors are used to monitor the soil, crops, pesticides, temperature,
light, moisture, humidity, etc. Readings from the different sensors are
reached to microcontroller, which sends the information to cloud where
it feeds to the analysing tools and visualize in required formats.
Analysis of the sensors, data leads the identification of possible
anomaly, if there exists, a quick response is initiated to resolve the
issue, and further, it again assesses the percentage of anomaly resolver.
On the basis of the assessment of such parameters, we can predict the
possible production of the yield [14].
4.1 Hardware Requirement

Nowadays, a technological advancement has taken place in agro-
products production. IoT is one of the important technology which is
helping farmers in various means. However, various electronic devices
like sensors have been integral part of today’s farming.
4.1.1 Sensors
Sensors can be mounted at drones, robots, with the trees, weather
stations and can be remotely controlled by mobile applications. A
sensor-based data capturing is shown in Fig. 3.
Fig. 3 Sensor-based data capturing
There are some common sensors listed in Table 1 which are very
common in agricultural.
Table 1 List of agri-sensors [15]
S. Agri-sensors Functional implications

No.
1. Optical-sensor Optical sensors are uses to sense soil properties (i.e.
moisture, organic contents, clay, etc.) through light. Optical
sensors can be mounted on robots, drown, etc.
2. Location-sensor Longitudinal–latitudinal–altitudinal position in a specific
area can be determined with this sensor
3. Air flow sensor Permeability of the air is measured through this sensor, and
these sensors can be used in both mobile and fixed places
4. Mechanical- Mechanical resistance and soil compaction are measured
sensor with this sensor
5. Electro Chemical components of the soil can be detected with the
chemical-sensor help of such sensors (pH level, nutrients in soil)
6. Dielectric Such sensors are uses to measure dielectric constant of the
moisture sensor soil to assess the soil moisture
4.2 Software and Algorithms
Various advanced data analytics algorithms (i.e. machine learning, big-
data, cloud computing) are the centre of gravity of the IoT-enabled
agriculture applications. A wireless network is used to connect all the
devices. This is very useful network for the peer-to-peer transmission
of the data specially when Internet connectivity is not consistent. A
detailed explanation of the machine learning-based algorithms is given
in Sects. 3.3 and 3.3.1.
4.3 Predictive Models for Yield

It is very uncertain to predict the crop yield because of unreliable
weather conditions. Thus, researchers are working over some
sophisticated yield prediction models with the help of different
technologies. Predictive models do analysis and observe the hidden
patterns, and on the basis of such patterns, we try to gain insights that
what will be the outcome of pattern extraction from the data (i.e.
forecasting of consumer behaviour, price, yield, weather and demand).
Machine learning is one of the key tools to develop such predictive
models. On the basis of different models, it can be established that the
climate change is impacting the yield production of different crops [16].
A parametric yield prediction model was presented by Jichong et al.
[17]. In this model, OLS regression was used over the growing degree
day (GDD) parameter which is about the time spend by plant in a
specific temperature range. In this model, researchers explored
different temperature bends and variation in plant growth. In [18], a
crop yield assessment model was developed by dividing crop growth in
four different segments specially in winter session. Initially, the Google
Earth Engine was used to integrate data like soil, remote sensing data,
climate data, etc., and interestingly model was able to predict one and
half month before the harvesting time at national level and error rate
was 0.75 only. However, various studies have established the superior
performance of the support vector machine, Gaussian regression,
random forest which are best performing in yield prediction [18]. In
[19], crop yield prediction models were presented. This model is based
on the crops of Andhra Pradesh’s. A rainfall model was developed to
predict the amount of rainfall during monsoon session. This model was
based on modular neural network and with the help of support vector
machine classification and amount of rainfall prediction the amount of
yield prediction is predicted. In [19], an artificial neural network-based
model was proposed for soybean and Maryland corn yield prediction in
odd climate conditions. In this research, USDA NRCS soil rating and
rainfall data were used. The learning rate of the proposed model was
0.90. According to this research, ANN network is best to predict
soybean and corn yields in Maryland climate. In [20], a corn yield
prediction model was proposed using long short-term memory (LSTM).
The model was processing hourly weather parameters and predicting
country-level time series-based corn yield prediction. In [21], wheat
yield prediction model is proposed using multilayer online soil data. XY-
fused network and counter-propagation ANN along Supervised
Kohonen was used. The soil and crop parameters were used with the
help of satellite imagery. According to this study, supervised Kohonen
network is best performing in yield prediction. In [22], a field-scale
maize yields prediction model that is proposed using time series
weather inputs. In the proposed model, stochastic disaggregation, and
nonlinear regression were used for different objectives. The predicted
variance of yields was 28% to 33% during October to December. In
[23], a crop predictive model for Indian climate is proposed. The
proposed model was based on both linear and nonlinear models, and
the crop prediction was done for two states of India (Telangana and
Andhra Pradesh). A crop yield prediction system is given in Fig. 4.
Another random document with
no related content on Scribd:
The Project Gutenberg eBook of The girl from
Bodies, Inc
This ebook is for the use of anyone anywhere in the United States
and most other parts of the world at no cost and with almost no
restrictions whatsoever. You may copy it, give it away or re-use it
under the terms of the Project Gutenberg License included with this
ebook or online at www.gutenberg.org. If you are not located in the
United States, you will have to check the laws of the country where
you are located before using this eBook.
Title: The girl from Bodies, Inc
Author: Len Spencer
Illustrator: Virgil Finlay
Release date: May 3, 2024 [eBook #73523]
Language: English
Original publication: New York, NY: Ziff-Davis Publishing Company,

1956
Credits: Greg Weeks, Mary Meehan and the Online Distributed

Proofreading Team at http://www.pgdp.net
*** START OF THE PROJECT GUTENBERG EBOOK THE GIRL

FROM BODIES, INC ***
THE GIRL FROM BODIES, INC.
By LEONARD G. SPENCER
It's a darned shame that just

when we get past the
experimental stage and learn
how to have fun, the old body
starts wearing out. Now what if
there were spare parts for sale?
What if you could get them
installed on demand? A new
heart, a new liver, a new—well,
just suppose!
[Transcriber's Note: This etext was produced from

Fantastic October 1956.
Extensive research did not uncover any evidence that
the U.S. copyright on this publication was renewed.]
"The trouble with bodies," said the new rub-down specialist at the
Gotham Baths, "is that after a while they just wear out."
"Glmph," said Hugh Horner as the skin-sleeking oil was applied
liberally to his face, making a drawn-out reply impossible.
"Ain't it funny, though," the rub-downer said, "how you can buy a new
set of piston rings for your car or a mainspring for your wristwatch or
a new gizmo for the old lady's mix-master, but you can't even buy a
new appendix, if you should need one, for yourself."
The quick hands left Horner's face and began to knead the sagging
muscles in his pectoral region. "If you look at it that way," Horner said,
"you have a point." He was alone in the massage room with the
attendant. He felt worn and drained out, as he always did at the end
of a heavy week's work at the office. A steam bath and a massage
helped, but he had to admit it: he wasn't as young as he used to be.
"Of course I have a point, Mr. Horner," said the attendant. "Folks
spent all that money on machines, what I mean, and almost nothing
on themselves. Tell me what happens when a guy develops a bad
ticker—Wait, I'll tell you what happens. He sits somewhere in a soft
chair, on a porch maybe, sucking on a dry pipe and waiting for the
next attack, which will probably kill him."
"I've heard pleasanter talk," Hugh Horner said in sudden distaste.
"What's the matter? Afraid of the truth?"
"Now really!" said Horner.
"How old are you, Mr. Horner? Forty-five?"
"I'm forty-seven," Horner admitted. His age, thus objectively stated in
his own voice, came as a mild shock. Forty-seven! He was virtually
middle-aged.
"Forty-seven! How many years before you change the car's battery?"
"Why, two or three, I guess."
"The tires?"
"Every twenty-five thousand miles. That would be about three years."
The attendant leaned down over him, still kneading the flesh of his
chest. "How much you got in the bank, Mr. H.?" he asked in a tight
whisper.
"I don't see where that's any business of yours," Horner replied in a
shocked voice.
"You get a car on time, it's the finance company's business, isn't it?
You take out a mortgage, it's the bank's business—right?"
"Yes, but—"
"How much, then?"
"Well, er, six thousand dollars."
"Joint account with your wife?"
"Y-yes."
"Happily married?"
"Now, just a minute!"
"Are you or aren't you?"
"Yes, I suppose so."
"You suppose so!"
"Yes, I'm happily married. Naturally, Jane isn't exactly the same girl
she was twenty years ago, when we were married. She's put on
some weight and she's got wrinkles and she's not exactly a sweater
girl—"
"I see. Any children?"
"No, we were never blessed—"
"Blessed, is it? Well, that's good. No children. I think you'll do, Mr.
Horner."
"Do? Do for what?"
"Congratulations, sir," the rub-down man said, smacking some oil on
Horner's abdomen and squashing the flesh around to show Horner
how soft he'd become.
Horner said, "Say, what happened to George, anyway." George was
Horner's usual attendant at the Gotham Baths.
"George wised up. He's out getting a body job."
"Oh, something happen to George's car?"
"Not his car."
"I'm afraid I don't understand."
"He's getting a body job," said the attendant. "He's getting a new
body."
The hands went slap-slap against Horner's abdomen. He could hear
the attendant's heavy, regular breathing. "Ha-ha," he said. "You're
pulling my leg."
"I just now explained—"
"You said George was out getting a new body. That's a joke, isn't it?"
"It's no joke to George. It's costing him four thousand dollars, all the
money he has. But he thinks it's worth it. Wouldn't you?"
"A new, er, body, you mean?"
"Yes. To start life at age twenty-five again, aware of all your mistakes,
your short-comings, your—"
"All right," Horner said finally, "that's enough. I've been lying here and
listening because I've had no choice, understand? But you've worn
that joke out, fellow. I wish you'd stop."
The masseur mumbled something under his breath, then said, "Well,
that does it on the front side. Care to roll over?"
"Yes," said Horner dutifully, and did so. He thought: funny, the way
this bird delivered that new body pitch. Such a straight face. So
utterly serious, almost as if he were interviewing me. The silence
stretched. Horner regretted having asked the attendant to stop his
yarn about new bodies. He finally said, in defeat, "Er, about what you
were saying—"
"You want an appointment? That's what I'm here for."
"An appointment? With whom?"
The attendant wiped his hands on a large towel and tossed its twin to
Horner. From somewhere, he plucked a neat white business card and
gave it to Horner. The card said:
BODIES, INC. By appointment only.
There was a telephone number and an address out on Long Island.
There was nothing else.
"Three thousand is what it will cost you," the attendant said. "You're
lucky it's a joint account you have."
"Three thousand dollars!" gasped Horner. "For what?"
"For a new body, naturally. Twenty-five years old and in sound health.
Fit as a pin, you're guaranteed that. I think it's a bargain."
"But three thousand dollars—"
"What kind of car you drive?"
Horner told him.
"Buy it brand new?"
"I never buy second-hand cars," Horner told him haughtily.
"Then it cost you damn near as much as a new body is going to.
What are you complaining about?"
Horner clucked an answer and then was told he could go to the
locker room and climb into his clothing. He tipped the usual fifty
cents, showered, dressed in his street clothing. He did all this, trying
not to think about what he had heard—but the more he tried not to
think about it, the more he did think about it.
Calling himself a fool, he returned to the massaging rooms. He poked
his head inside the room in which the new man had given him a rub-
down.
An attendant with a stocky build and shell-rimmed glasses stared out
at him, squinted myopically, and smiled. "Evening, Mr. Horner," he
said.
It was George, who had given Horner his weekly massage every
week for the past five years—except tonight.
"Why, you're here!" blurted Horner.
"Sure am, sir. Wondered why you were late. Go ahead and undress,
now. I'll reserve your usual table...."
"But I just had my massage."
"Oh?" said George, trying to make his voice sound indifferent. "Trying
one of the other masseurs?"
"Not at all," snapped Horner. "You weren't here. Well, were you?"
"Never even stepped out. Been here all night," George said.
"But the other man, the new man—"
"No new man, Mr. Horner, sir. Haven't put on a new man in six-seven
months. I'd know, wouldn't I?"
"You'd know," said Horner slowly, after a silence.
"Something the matter, sir?"

"It's nothing. Nothing."
Horner got out of there very quickly. He took a cab home, which was
unusual for him. If George and his nameless friend had been playing
an elaborate practical joke, they had also been playing hob with
Horner's digestion. For now a hot sensation flooded his middle—his
damned ulcer acting up. Ulcers, he thought with a sudden wry smile,
ulcers and what else? You're forty-seven, Horner. A mildly successful
life, a good marriage, a middling business, no children, no
outstanding debts—any regrets?
Yes, Horner thought. Regrets. His ulcer was a regret. He had to be
careful what he ate, couldn't drink much. His rising blood pressure
would one day be a regret, even if it wasn't yet. And generally,
vaguely, his insignificance was a regret. He was not a meek man, but
he was no Tarzan of the Apes. He was not a small man, but he was
no Goliath. He was not a low-brow, but he was no Einstein. He was
not without an eye and some appeal for women, but he was no Don
Juan. He sighed, knowing you could extend the list indefinitely. Hugh
Horner, small businessman. Hugh Horner, small man.
"Here's your address, Mac," the cab driver said.
Horner got up with a start. He realized he had been sitting there for
some time with the cab perfectly still. He somehow sensed that time
had passed, more time than the thirty-odd minutes it would take a cab
to deliver him to his home on the other side of the Brooklyn-Battery
Tunnel.
"Where—where are we?" he asked the cabbie. For some reason, he
fingered the business card in his pocket. The one the new masseur,
the masseur who apparently did not exist, had given him.
The cabbie, shrugging, told him an address which was not
immediately familiar. Then, with a sudden quickening of his heart,
Horner realized it was the address on the business card in his pocket.
"You mean," Horner demanded, "we're on Long Island? I don't
remember telling you to take me here."
"Well, I didn't dream it up myself, Mac," the cabbie said. "Look, I don't
care if you get out or you don't get out. The flag is still down and I'm
still making money. So, what'll it be?"
"I ought to call my wife," Horner said.
The driver shrugged. "You getting off here?"
Slowly, Horner nodded. He looked outside. He saw night darkness, a
dimly lit driveway, a hemlock hedge twelve feet high.
"Sign said 'Positively no vehicles,'" the cabbie told him. "So I guess
you walk from here."
"I guess I walk," Horner said. He consulted the taxi meter, took four
dollar bills from his money clip and a half dollar in change from his
pocket. Then he got out.
The cab door closed. The driver put the clutch down, then up, and the
cab rolled away into the darkness. Horner lit a cigarette. It tasted
harsh and bitter, stale. The darkness engulfed him and a pulse
hammered, of all places, in his right leg. He felt all at once old—or at
least aging. He sighed and it was not a sound a young man would
make. In the darkness on the unknown road, he longed for his youth,
his lost youth. Then he walked resolutely up the dimly lit driveway
flanked by the high hemlock hedge.
The door-knocker was brass, and Horner let it fall. It made a

resounding noise and the door opened within a second, as if
someone were standing half a foot away on the other side with no job
but to admit Hugh Horner the instant he knocked.
"Come in, Mr. Horner," the girl said. "Naturally, we were expecting
you."
She was tall and she wore a cashmere sweater, loose but not so
loose that it failed to reveal high, maidenly breasts. She wore a skirt
not provocatively tight, but tight enough to suggest the good thighs
that she had. Her hair fell almost to her shoulders in abundant auburn
waves. She had a lovely face and Horner thought she was about
twenty years old.
"You were expecting me?" Horner said.
"Of course. You see, Bodies, Inc. carefully screens its applicants...."
"But I didn't apply!"
"Ah, but we knew you were going to. We have to be sure of our
clients. Because if a single client decided to talk, we'd be out of
business."
"The authorities?"
"Certainly. But since you're here, we can get down to business at
once. You have the three thousand dollars with you?"
"Why, no. No, I don't."
"Bankbook?"
"Yes, I have that."
"It's good enough. Tomorrow we can take your identification papers,
driving license and so forth, and get the money ourselves. That is,
unless they know you personally at the bank?"
Horner said that he did his banking by mail. He supposed they were
going to forge his signature, but made no comment because he had
decided, all at once, to call the whole thing off.
"See here," he said. "This is a little awkward. But you can trust me not
to talk."
"What's a little awkward?"
"I—I've decided not to go through with it," Horner said lamely. "My
wife, my friends...."
The girl said nothing. She took two steps forward, placed her arms
around Horner, and kissed him. She wore a subtle perfume. She was
beautiful. Her lips were soft and warm, inviting. Her lips were hot. Her
lips burned....
Horner broke away breathlessly. His heart was pounding. He knew
his face was flushed, he could feel it. His legs were unsteady. He
wanted to respond, but his energies were dissipating in the hard-
pumping heart, the trembling limbs, the flushed face. It was a middle-
aged response. It lacked the drive and direction of youth.
"Did you like that?" the girl asked, taking one of Horner's hands and
holding it.
"Yes," came his breathless reply. "Oh, yes! I liked it."
"But you didn't...."
"Respond? I have a wife."
"That wasn't the reason."
"We're happily married!"
"And I like this sweater I'm wearing very much, but I have others and
will wear others."
"The mores of our society...."
"Mores baloney! You were just plain scared. Middle-aged scared.
Look at you. You're soft and you're getting wrinkles. Do you think I
was really attracted to you? Do you think that's why I kissed you? No,
you fool. That wasn't the reason."
"Then you...."
"Wanted to make this point. Wanted to show you you're old, too old to
enjoy the most obvious pleasures of a younger man's life. Twenty-
five, Mr. Horner! That's the age! The age not of boyishness but of
mature youth! Twenty-five! The perfect age for you, and you know it."
She smiled at him. It was a deliberately sexy smile, a come-on, an
invitation which Horner, under the circumstances, had to decline. "Are
you convinced?" she said.
"That I'm not as young as I used to be? Of course."
She gave him a deliberately daughterly kiss, pecking at his temple
with her soft warm lips. "Then you're ready to go to the observation
room."
The observation room, thought Horner. Did he do the observing, or
was he observed? He sighed. It was not a young man's way of
expressing what Hugh Horner felt. He knew it was not. He said
slowly, bleakly, "I'm ready for the observation room."
The girl did not even nod. She had known he would be ready all
along.
It was a small, utterly bare room with three walls of dull gray metal
and the fourth of dazzling floor-to-ceiling glass. On the other side of
the glass was a similar room—except that it was furnished with a
single bench running across its length.
Men were seated on the bench. Young men, apparently staring at
Horner and his lovely companion.
"They can't see us," the girl explained. "One way glass."
"But do they, er, know why they are here?"
"Naturally. Everything's on the up-and-up with Bodies, Inc., morally if
not legally."
"And they are...."
"Your choice, Mr. Horner. As you can see, there are eight young men
in there, each twenty-five years old, each guaranteed in good health,
each perfectly willing to switch identities with you. I must tell you in
advance, however, that the switch is quite permanent. There is no
recourse. You understand?"
"Yes, but...." Horner looked at the eight men who could not see him,
and lapsed into silence. The eight all looked like sound specimens, all
right. All seemed healthy and alert, even cheerful. Horner said,
somewhat suspiciously, "My reason for wanting to switch places is
obvious. And theirs?"
It was a good body. It looked as though it would last for years.
The girl licked her lips before she spoke. They were very nice lips.
They were delicious lips. Horner had tasted them. He was suddenly
reminded of a magician who makes diverting passes with one hand
while performing his magic with the other. "Money," the girl said
laconically.
"Money? But I'm only paying three thousand dollars. Surely a man
wouldn't surrender his youth for such a sum!"
"Our regulations call for a man's total savings. In your case, three
thousand dollars. But most of our clients are extremely wealthy, Mr.
Horner. Now, since half of the fee goes to the youth who will become
Hugh Horner while we keep the other half...."
"But fifteen hundred dollars only!"
"I should have said it goes into a pool. A yearly pool, you see. The
average last year was four-hundred sixty-five thousand dollars, Mr.
Horner. Don't you think some young men would be willing to
surrender twenty years of their lives for half a million dollars?"
"I wouldn't if I were young," Horner said at once.
"Between you and me, that's because you aren't. But it's their choice
to make, and it's a free choice. Now, have you made a selection?"
Horner looked at the eight men again, and shrugged.
"I see," the girl said. "And I agree. They're all choice specimens, is
that what you're thinking? All strong, all healthy, and all will probably
be in better shape than you are, twenty years from now."
"Do I get some kind of a guarantee on their health? I mean, what if ...
if I should pick one of them with an incurable disease or something?"
Although he asked this very practical question, Horner still hardly
expected to go through with anything as incredible as switching
bodies with one of the young men on the other side of the glass
partition. After all, he told himself for the tenth time, such things just
weren't possible. This was either an elaborate joke or an elaborate
dream. He decided—hopefully—that it was the latter. He recalled that
the doctor had given him reserprine to calm his nerves recently, and
the doctor had told him that one of the side effects of reserprine was
an abundance of nightmare.
"That's it," he said. "Reserprine."
"What did you say?" the girl asked him, an amused look on her face.
"Er, I said, that one's fine," Horner blurted, pointing at random at one
of the men on the other side of the glass partition.
"Good," the girl said. "Then everything is ready." She touched a
section of the wall and the dazzling glass sheet abruptly went
opaque. This lasted for some five seconds, then the wall became
transparent again.
All but one of the men had disappeared. Horner assumed it was the
individual he had singled out quite at random.
"Now really ..." he began.
"Look at me," the girl said.
That was easy. She was beautiful.
Her eyes grew very large. Incredibly large. They filled her entire head.
They filled the room. They were two enormous blue pits. Horner
jumped into both of them just before he fell into a deep hypnotic
sleep.
His hands were raw and bleeding. His first thought was that the
guards would know something was wrong when they saw his hands.
He was down on his knees in foul-smelling dirt, but his head scraped
the low ceiling. He was digging mechanically with his bare hands. He
had had a shovel, but it had been lost in a slight cave-in.
"Hey, Lonnie!" a harsh whispering voice called. "Stop dreaming, for
cryin' out loud. If we don't do it tonight, we'll never get another
chance. Forbish is out."
"What do you mean he's out?" called back Horner, whose name now
seemed to be Lonnie.
"You know what I mean. Out. Another cell-block. Forbish got a mouth
like the Holland Tunnel. What I mean, if he ain't here to cash in on the
deal, he's gonna spill it. And fast. How you comin'?"
"I'm digging," Horner responded. "I'm digging ... and digging." He was
doing that, all right. The work should have been tremendously tiring,
should have exhausted Hugh Horner in his run-down forty-seven-
year-old body. But he found it almost exhilarating. He looked at his
hands. Dirty hands, and bloody. But large—larger than they should
have been. Horner had had small hands, almost delicate hands. He
dug and dug, thinking.
Either it was another reserprine dream—or he wasn't Hugh Horner.
Then was he the man whom he'd selected—more or less at random?
But that wasn't possible, for the man in question had been in the
Bodies, Inc. establishment on Long Island—unless, somehow, that
had merely been a projected image of the man, like three-
dimensional television. Then ... where was he?
"Want me to take over, Lonnie?" demanded the harsh whisper. For
the first time, Horner realized that it was not close by. It was a loud
whisper and it came from a considerable ways off. Wanting time to
think, Horner said, "Yes. All right."
He backed out of the tunnel slowly, awkwardly, his body stiff. Stiff, but
not painful. Hugh Horner's limbs would have ached terribly in this
cramped position, but Lonnie's did not. Lonnie scurried more rapidly
now—backwards and not minding it at all—out of the tunnel. The
walls of the tunnel, Horner observed, were of bare soft earth. If his
elbows or knees struck them, some of the earth sifted down, and
sometimes a rock. He had the sudden impression that the tunnel had
been dug over a considerable period of time with crude implements
or by hand.
Finally, Horner emerged into a small square room. There were two
bunks, one over the other, he observed as he stood up. The walls
were bare plaster. There was a sink and a lidless toilet. There was a
small mirror. Only three of the walls were plaster. The fourth
consisted of a grim row of vertical bars.
He was in a prison cell.
He gazed about wildly. He wanted to scream. He didn't understand

how this could be, but understanding was decidedly secondary. He
looked at his bloody hands. It was his own blood—Lonnie's, that is—
but it was symbolic to him. A man was sitting on the edge of one of
the bunks, smoking. He was watching Horner. He was a short man
with immense shoulders. He wore gray denim and Horner did not
have to be told it was a prison uniform or that his clothing was
identical.

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Internet of Things and Analytics for Agriculture Volume

Cognitive Computing for Risk Management 1st Edition

Cognitive Computing for Risk Management 1st Edition

Fundamentals of Internet of Things 1st Edition Sudhir

The Industrial Internet of Things (IIoT): Intelligent

Internet of Things: Energy, Industry, and Healthcare

Connecting the Internet of Things: IoT Connectivity

Data Analytics in the Era of the Industrial Internet of

Studies in Big Data

Internet of Things and Analytics for

ISSN 2197-6503 e-ISSN 2197-6511

© The Editor(s) (if applicable) and The Author(s), under exclusive

The use of general descriptive names, registered names, trademarks,

This Springer imprint is published by the registered company Springer

Raghvendra Kumar is working as Associate Professor in Computer

Souvik Pal , Ph.D., MCSI, MCSTA/ACM, USA; MIAENG, Hong Kong;

Vijaypal Singh Dhaka

Manuj Kumar Hazarika

Jason Elroy Martis

Brojo Kishore Mishra

Amit Kumar Pandey

Immanuel Zion Ramdinthara

Manoj Kumar Sharma

Rajveer Singh Shekhawat

Ansel Elias Stanley

Functional Framework for IoT-Based

Rajveer Singh Shekhawat

Keywords IoT – Food chain – Sensors – Wireless – Agro-products –

Fig. 1 IoT-enabled agriculture framework

2 Scope of IoT in Agriculture

(c) Internet—is helpful in the communication of servers and

(d) Back End Services—consist of end-user access, control, remote

IoT has a substantial impact on new-age agriculture technologies,

2.1 Irrigation and Fertilizers

2.2 Disease Control

2.3 Precision Farming

2.4 Crop Yield

2.5 Storage and Transportation

2.6 Livestock Monitoring

2.7 Farm Machinery and Maintenance

3 IoT Framework for Smart Agriculture

Fig. 2 Detailed IoT framework

3.1 Hardware Requirement

(3) Electromagnetic Sensors: These sensors are used to measure

(4) Electrochemical Sensors: These sensors are used to measure

(9) Water-Level Sensors: These sensors are helpful to monitor the

(10) IPR Sensors: These sensors are used to monitor animal

3.1.2 GPS Module

3.3 Software and Algorithms

3.3.1 ML-Based Image Processing Algorithms

(2) Convolutional Neural Network (CNN): As compared to other

(3) Long Short-Term Memory (LSTM): This network works well

(6) Multitask Learning (MLT): In this model, a representation is

(7) Deep Recurrent Q-Network (DQN): DQN is the variation of

3.4 Data Storage