CHAPTER - 1

INTRODUCTION
The internet of things (IoT) is a promising technology that provides answers to problems in a variety
of sectors. Kevin Ashton of MIT’s Auto-ID lab invented the term in 1999. The IoT is a network of
billions of connected devices that can sense, collect, and transmit data without human intervention,
affecting a wide range of industries including health care monitoring, building automation, logistics,
connected vehicles, smart city infrastructure, smart grid, smart home, smart retail, smart agriculture,
and smart farming. This chapter describes the use of IoT in Smart Agriculture, including an
introduction to IoT, Wireless Sensor Networks, Smart Agriculture Using Wireless Sensor Networks,
Motivation for Research, Challenges, Research Objectives, and Research Contributions (Haseeb et
al., 2020).

Agriculture has experienced multiple revolutions, including plant and animal domestication, crop
rotations, and the “green revolution.” ICT is viewed as the catalyst for a fourth agricultural revolution.
Smart farming is a management approach that employs cutting-edge technology to measure, monitor,
automate, and evaluate operations. It is managed by sensors and controlled by software. Because of
population expansion, growing use of technology, and climate-smart agriculture, smart farming is be-
coming increasingly vital. It is a cyber-physical system that controls and manages the whole farm
system using smart devices connected to the Internet. Traditional tools are improved by smart
gadgets, which provide autonomous context awareness, built-in intelligence, and the capacity to
undertake autonomous or remote operations. Humans are still involved in the process, but at a higher
cognitive level, with robots performing the majority of operational activities(Alghazzawi et al.,
2021).

The Internet of Things (IoT) enables wireless hardware to share data via a network, resulting in a
significant increase in the number of electrical, connecting devices in the 1980s and 1990s. M2M and
IoT communication have facilitated the growth of linked devices, with Cisco forecasting that there
would be 50 billion connected devices by 2020. The Internet of Things is envisioned as the foundation
of a networked, safe, intelligent, and inventive civilization of the future. The Internet of Things (IoT)
is an ubiquitous computer system that enables devices to connect directly with one another and
exchange related information, allowing humans to concentrate on choices and actions rather than
filtering and integrating data.
 Smart Agriculture Introduction

Wireless Sensor Network (WSN) technology has improved greatly, allowing the use of motes and sen- sor nodes
to monitor ecological occurrences across a vast geographic area. By engaging with a gateway, sensor nodes may
communicate wirelessly and relay data to a base station or coordinator node. WSNs can monitor a broad range
of surroundings and acquire exact information since the communication is based on numerous sensors. The
detecting, storage, processing, and transmission capabilities of sensor nodes have increased. WSNs have found
applications in the military, agriculture, sports, medicine, and industry. Precision agriculture (PA) aims to
enhance field management by avoiding the same manage- ment routine no matter what the site conditions are.
PA lowers pesticide waste while also ensuring crops receive the nutrients they require, leading in efficient,
ecologically responsible agriculture. PA is a management strategy that employs information technology to
improve agricultural quality and output. It consists of five steps: data collection, diagnosis, data analysis,
precision field operation, and evalua- tion. WSNs are used to boost agricultural production and anticipate crop
health and product quality by predicting irrigation plans based on weather and soil moisture. Additional sensor
nodes can be added to the present WSN to improve the monitoring characteristics of the smart farming system
and make the network scalable.

Fig. 1.1 AI Smart agriculture control Device

The integration of big data analytics into the agricultural sector, often termed as "smart agriculture," has
emerged as a transformative approach to address the complex challenges faced by modern farming
practices. In recent years, the proliferation of advanced technologies, coupled with the exponential
growth of data generated from various sources, has paved the way for innovative solutions to optimize
agricultural production, enhance sustainability, and improve food security.

Dept of CSE,SJCIT 2 2024

 Smart Agriculture Introduction

Smart agriculture leverages big data analytics to harness insights from vast amounts of data collected
from sensors, satellite imagery, weather stations, and other sources, enabling farmers to make data-
driven decisions in real-time. WSN deployment techniques, measurement times, routing protocols,
energy ef- ficiency, cost, communication range, scalability, and fault tolerance have all been challenges.
Although dispersed sensor node deployment might assist increase network lifetime, selecting a
distribution zone can be problematic. WSNs are battery-powered, which eliminates the requirement for
connections to the main power source. It is critical to decrease power depletion and extend battery life
in order to reduce power depletion and extend battery life(Rao et al., 2022).
IoTs give important data and learning opportunities, and a WSN created a system to examine plant-
related sensor data for social events. This review employed WSN data to discover learning through data
mining. Leaf spot disease was monitored using remote sensors and field level identification. As an
Internet-based observation approach for IoTs, distributed computing was proposed. Knowledge mining
was utilized to extract useful data and learning; however this activity might jeopardize the information
provided by IoT. Data mining uses IoTs to extract useful and actionable knowledge from enormous
amounts of data, hence addressing Information Creation Mechanisms. The electronic framework assists
an executive in tracking the water requirements of yields and may be enhanced to estimate harvest water
requirements. The suggested system is divided into three layers: the natural layer information, the infor-
mation and correspondence layer, and the application layer. The ecological information collecting layer
gathers data from sensors and device control on natural elements, and the framework layer utilizes the
gathered data to track and monitor harvesting and deliver information to the board(Burugari et al.,
2021).
The IEEE 802.15.4 standard for control and control systems for wireless personal area networks is
central to Zigbee connectivity (WPANs). These WPANs operate on wavelengths of 868 MHz, 902-928
MHz, and 2.4 GHz, with a data rate of 250 kbps for occasional two-way information transfer. Zigbee is
a low-power communication technology that can manage and track applications with a range of 10-100
meters. It is more dependable and straightforward than other short-run remote sensors, and it may be
enlarged to need the use of several hubs to interface with one another. A Zigbee network is composed
of three components: an organizer Zigbee, a modem, and an end computer, with one facilitator serving
as the system’s root and extension. Zigbee switches are used to transfer data between devices, hence
minimizing battery use. The number of switches, facilitators, and end machines is determined by the
system type. The Zigbee module for wireless channel data transfer is seen.

Dept of CSE,SJCIT 3 2024

Smart Agriculture Introduction

In the realm of agriculture, AI is revolutionizing traditional farming practices, ushering in an era of

smart agriculture. By harnessing the power of artificial intelligence, farmers can make data-driven
decisions to optimize crop yields, reduce resource waste, and enhance sustainability. AI-driven
technologies such as drones, sensors, and satellite imagery enable farmers to monitor crops and soil
conditions with unprecedented accuracy. Machine learning algorithms analyze this data to provide
insights into crop health, pest infestations, and environmental factors, empowering farmers to take
timely actions to mitigate risks and maximize productivity. Moreover, AI-powered predictive analytics
forecasts crop yields, market trends, and weather patterns, enabling farmers to plan and manage their
operations more effectively. Additionally, robotic automation and autonomous vehicles streamline
farming tasks, increasing efficiency and reducing labor costs. By integrating AI into agricultural
practices, farmers can achieve greater profitability, environmental stewardship, and resilience in the
face of evolving challenges.

Dept of CSE,SJCIT 4 2024

CHAPTER - 2
LITERATURE REVIEW
A literature review on smart agriculture using AI encompasses various facets of this rapidly evolving
field, ranging from technological advancements to practical applications and their implications for
agricultural sustainability, productivity, and socio-economic development.

Firstly, studies have extensively explored the role of AI technologies such as machine learning, remote
sensing, and Internet of Things (IoT) devices in transforming traditional agricultural practices into data-
driven and precision-based approaches. For example, research by Atzori et al. (2017) highlights how
IoT sensors and AI algorithms enable real-time monitoring of soil moisture, temperature, and nutrient
levels, facilitating precise irrigation and fertilization management. Similarly, Khan et al. (2019)
demonstrate the effectiveness of machine learning models in predicting crop diseases and pest
infestations based on environmental parameters and historical data, aiding in early detection and
targeted intervention strategies.

Furthermore, the literature emphasizes the potential of AI-driven predictive analytics for optimizing
agricultural production and supply chain management. By integrating historical weather data, market
trends, and agronomic knowledge, AI algorithms can forecast crop yields, market demands, and
commodity prices with remarkable accuracy (Gebbers & Adamchuk, 2010; Popovic et al., 2020). This
predictive capability empowers farmers and agribusinesses to make informed decisions regarding crop
selection, planting schedules, and marketing strategies, thereby enhancing profitability and market
competitiveness.

Moreover, scholars have explored the socio-economic implications of AI adoption in agriculture,

considering factors such as access to technology, digital literacy, and the equitable distribution of
benefits. While AI has the potential to revolutionize smallholder farming and empower rural
communities, concerns have been raised regarding the digital divide and disparities in technological
access (Qadir et al., 2021). Additionally, the integration of AI technologies in agriculture raises ethical
considerations related to data privacy, algorithmic bias, and the socio-cultural implications of automated
farming practices (Giller et al., 2017; Tu & Hsu, 2021). Furthermore, literature in the field of smart
agriculture often addresses the environmental sustainability of AI-driven farming practices. While AI
can optimize resource use, minimize chemical inputs, and reduce environmental impact through
precision agriculture techniques, its widespread adoption may also exacerbate concerns related to
energy consumption, e-waste generation, and ecological disruption
Smart Agriculture Literature review

Therefore, research efforts are directed towards developing AI-powered solutions that prioritize
environmental conservation and promote agroecological principles.

In conclusion, the literature on smart agriculture using AI underscores its transformative potential in
revolutionizing agricultural systems worldwide. From precision farming and predictive analytics to
socio-economic considerations and environmental sustainability, interdisciplinary research in this field
continues to expand our understanding of the opportunities, challenges, and implications of AI adoption
in agriculture. By addressing knowledge gaps, fostering interdisciplinary collaborations, and integrating
stakeholder perspectives, future research endeavors aim to harness the full potential of AI to address
global food security, rural development, and environmental sustainability challenges.

Dept of CSE,SJCIT 6 2024

CHAPTER - 3
METHODOLOGY

Hybrid Sensor, Region Division, Node Deployment, Periodic Threshold Checking, Event Checker,
Rout- ing Protocol, and Performance Evaluation were the seven modules presented for the proposed
system. During critical situations, the Region division module is utilized to find priority-based sensor
nodes for effective data transfer to the Base Station packet, resulting in optimal energy utilization.
Hybrid Sensor, Region Division, Node Deployment, Periodic Threshold Checking, Event Checker,
Routing Protocol, and Performance Evaluation are the seven modules presented in the proposed
system. The Hybrid sensor module collects data from the region using two sensors: temperature and
humidity. The network employs three types of dissimilar packet sensor nodes: Type-1, Type-2, and
Type-3. Type-1 sends data straight to the Base Station, but Types-2 and 3 use the best way to the
Base Station.

For effective data transmission, the proposed system splits the field into six regions, with the
outermost regions R-1, R-2, R-3, R-4, and R-5 placed far from the BS node. Type-2 and Type-3
nodes in the outermost zone use more energy to transfer data to the Base Station than Type-1 nodes
in the innermost region. This is owing to their proximity to the BS. Irrigation does not necessitate
constant data transfer to the BS, therefore nodes in the Periodic Threshold Checking Module monitor
environmental factors continually. Data transfer to the BS happens only when the detected value
falls within the user-specified range. However, threshold sensitivity has a limit, there- fore after a
given duration, a different routing method is used to convey the data. A Periodic Threshold Checking
Module is used by the farmer/user to gather temperature and moisture content data at each set time
period. To determine when to start the water system in the field, a periodic dynamic threshold system
calculates two state variables, surface temperature and moisture level.

The Event Checker Module validates multi-node events and sends them to the base station in priority
order, using a trust value to determine the optimum routing. Each node’s trust value is assigned by
the periodic table, which also routes data packets. Farmers can communicate data via wireless sensor
networks (WSNs). Precision agriculture (PA) is a management approach that employs data
innovation to increase quality and yield. Farm management will increase PA by synchronizing
appropriate seed nutrients and wasting chemicals used to control plants, pests, and infections. This
research examines the recent use of wireless networks (WSNs) in agricultural analysis and classes.
Smart Agriculture Methodology

It investigates different wireless communication systems, vitality-compliant and vitality-collection

approaches, as well as the relationship between early research and farming-based WSNs. It also
examines the problems and obstructions of WSNs in the horticulture field, as well as how these
approaches might be employed to handle IoT data. Sensors are utilized to collect inputs for
processing, including temperature and humidity sensors detecting and collecting data from external
conditions. Weather conditions are important in smart farming, with dry settings requiring more water
and wet situations requiring less water from canals(Kadam et al., n.d.).

We’ve picked a grass field with specific soil types. It is critical to explain the 9 step margins of soil
moisture tests in order to create an accurate soil moisture chart at varied depths. It is 30 cm long and
separated by 10 cm by sensors, allowing for near-constant forecasts. To get an IoT stage structure, this
study organized a soil moisture test using low-power equipment. Each neighbouring SN monitors the
battery speed and detects and saves the solar vitality acquired in order to process a vitality spending
profile. The most significant aspects are that SNs can range from tiny instruments to enormous
implanted levels, and that limited capability is required for sign planning, device identification, a
smartphone, a low memory level, and onboard access. These devices have the capability of wireless
exchange, making them suitable for a variety of applications.

To transport data to the Base Station packet, this work employs three types of packet sensor nodes:
Type-1, Type-2, and Type-3 (BS). All sensors are distributed in a specified land by packet (A*A) unit
square for real-time sensing and data transfer. For efficient data transmission, the field is split into six
regions, with R-1, 2, 3, and 4 nodes in the outermost regions and 6 nodes in the centre of the
innermost area. Type-3 nodes use more resources than Type-1 nodes, whereas Type-2 nodes use less
energy but are more powerful than Type-1 nodes. Because of their reduced energy use, type-1 nodes
occupy the innermost region. The network setup includes three types of homogeneous sensor nodes
that provide data to the BS and employ fuzzy-based grouping algorithms to increase efficiency and
network life span. A periodic dynamic threshold system can use the suggested model to determine two
state variables: surface temperature and moisture level. According to the unit’s hypotheses, both
sensor networks and the BS are static after organization, and the BS is located in the detector sector’s
middle situation.

Dept of CSE,SJCIT 8 2024

 Smart Agniculture Methodology

Fig 3.1. Smart sensing and monitoring in the realization of strategicdirections.

Spatial analysis techniques play a pivotal role in augmenting the analytical capabilities of big data in
agriculture, particularly in the context of precision farming. Geographic information systems (GIS)
facilitate the visualization and analysis of spatially referenced data, enabling farmers to discern spatial
variability in soil properties, crop performance, and environmental conditions. By leveraging spatial
insights, farmers can implement targeted interventions such as variable-rate application of inputs and
site-specific management practices to maximize resource efficiency and productivity.

Dept of CSE,SJCIT 9 2024

.CHAPTER -4
COMPARSION OF PROPOSED SYSTEM

A comparison of the proposed system with existing agricultural practices reveals significant
advancements and potential benefits in terms of efficiency, productivity, and sustainability. The
proposed system integrates AI technologies such as machine learning, IoT sensors, and predictive
analytics to revolutionize traditional farming methods into data-driven and precision-based approaches.
Unlike conventional practices that rely on manual labor and subjective decision-making, the proposed
system enables real-time monitoring of crop health, soil conditions, and environmental parameters,
providing farmers with actionable insights to optimize resource use and enhance crop yields.

One key advantage of the proposed system lies in its predictive capabilities, which enable farmers to
anticipate and mitigate potential risks such as crop diseases, pest infestations, and adverse weather
events. By analyzing historical data and environmental trends, AI algorithms can forecast crop yields,
market demands, and commodity prices with remarkable accuracy, empowering farmers to make
informed decisions regarding crop selection, planting schedules, and marketing strategies. This
proactive approach not only improves profitability but also minimizes production losses and market
uncertainties associated with traditional farming practices.Furthermore, the proposed system offers
scalability and adaptability to diverse agricultural settings and socio-economic contexts. Whether in
smallholder farms or large-scale agribusinesses, AI technologies can be tailored to meet specific needs
and challenges, providing customized solutions for sustainable agricultural development. Moreover, the
integration of IoT sensors and autonomous devices facilitates remote monitoring and management of
farm operations, reducing dependency on manual labor and optimizing labor efficiency.

However, it is essential to acknowledge the challenges and limitations associated with the proposed
system, including initial investment costs, technological barriers, and digital literacy requirements.
While AI-driven solutions hold immense potential for transforming agriculture, their widespread
adoption may be hindered by factors such as access to technology, infrastructure limitations, and
regulatory frameworks. Additionally, ethical considerations related to data privacy, algorithmic bias,
and socio-cultural implications must be addressed to ensure equitable and responsible deployment of
AI technologies in agriculture.
Smart Agriculture COMPARSION OF PROPOSED SYSTEM

In conclusion, while the proposed system represents a significant advancement in agricultural

technology, its success depends on overcoming implementation challenges and addressing socio-
economic and ethical considerations. By leveraging AI technologies to enhance efficiency, productivity,
and sustainability, the proposed system has the potential to revolutionize global agriculture and
contribute to food security, rural development, and environmental conservation efforts. Nonetheless,
collaborative efforts involving stakeholders from government, academia, industry, and civil society are
essential to realize the full potential of AI in agriculture and ensure its equitable and sustainable
deployment.

Fig. 4.1 Low financial outlay and work-

optimized diversified production in CSA.

Dept of CSE,SJCIT 11 2024

CHAPTER - 5
SCOPE AND APPLICATIONS

The proposed system aims to revolutionize traditional agricultural practices by integrating advanced
technologies such as artificial intelligence (AI), Internet of Things (IoT), and data analytics. Unlike
conventional farming methods, which rely heavily on manual labor and intuition, the proposed
system offers a data-driven and precision-based approach to agriculture, enabling farmers to make
informed decisions and optimize resource utilization

1. Precision Agriculture: The proposed system enables data-driven and precision-based farming
practices by integrating AI, IoT, and data analytics to optimize resource utilization and
enhance crop productivity.

2. Crop Management: It facilitates early detection and management of diseases, nutrient

deficiencies, and weed infestations through real-time monitoring of soil health, crop growth,
and environmental conditions.

3. Irrigation Management: The system optimizes water usage by monitoring soil moisture levels
and weather forecasts, leading to reduced water waste and improved water efficiency.

4. Pest and Disease Management: AI-powered algorithms predict outbreaks and recommend
suitable control measures, minimizing reliance on chemical pesticides and promoting eco-
friendly pest management strategies.

5. Crop Monitoring and Forecasting: It forecasts crop yields, market demands, and commodity
prices using predictive analytics, enabling informed decisions regarding crop selection,
planting schedules, and marketing strategies.
 CHAPTER - 6
CONCLUSION

In conclusion, the proposed system represents a significant advancement in agricultural technology,

offering a comprehensive solution to address the challenges faced by farmers and agribusinesses. By
integrating artificial intelligence, Internet of Things, and data analytics, the system enables data-driven
decision-making and precision-based farming practices, leading to higher crop yields, reduced resource
waste, and enhanced sustainability. The wide range of applications, including precision agriculture, crop
management, irrigation management, pest and disease management, crop monitoring and forecasting,
supply chain management, and market analysis, underscores its versatility and potential to revolutionize
the agricultural sector. Moreover, the system's emphasis on sustainability, innovation, and adaptability
positions it as a key enabler of growth and prosperity in the agricultural industry. Moving forward,
continued research, development, and adoption of such technologies are essential to realizing the full
potential of smart agriculture in addressing global food security, environmental conservation, and socio-
economic development challenges. By harnessing the power of technology and innovation, we can
create a more resilient, efficient, and sustainable agricultural system for the benefit of present and future
generations.
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