This document provides an overview of how Internet of Things (IoT) and big data analytics can be applied to smart agriculture management. It discusses key concepts like IoT, how sensor data is collected and analyzed in big data systems, and how this data can be used to improve decision making for smart agriculture. The document also outlines some of the challenges of implementing these systems at scale, such as technical complexity and economic efficiency concerns. Overall, the document examines how applying IoT and big data analytics can help modernize agriculture and increase productivity, yield, and supply chain optimization.

1. “Internet Of Things(IoT) And Big-
data Analytics In Smart Agriculture
Management”
Dhan Prasad Ghale
Master in CIS-I
Subject: Ethical and professional issues of IT
Nepal college of Information Technology (NCIT)

2. OVERVIEW
• Introduction
• Internet of Things (IoT)
• Big Data and Analysis Methods
• Smart agriculture
• Problem Statement
• Research
Methodology
• Results Analysis
• Future Application
• Implementation
challenges
• Conclusion

3. INTERNET OF THINGS (IOT)
• The internet of things, or IoT, is a system of
interrelated computing devices, mechanical
and digital machines, objects, animals or
people that are provided with unique
identifiers (UIDs)
• An IoT ecosystem consists of web-enabled
smart devices that use embedded systems,
such as processors, sensors and
communication hardware, to collect, send
and act on data they acquire from their
environments.
• IoT devices share the sensor data they collect by
connecting to an IoT gateway or other edge device
where data is either sent to the cloud to be analyzed or
analyzed locally

4. BIG DATA AND ANALYSIS METHODS
• Big Data is a collection of data that is huge in
volume, yet growing exponentially with time.
• It is a data with so large size and complexity that none
of traditional data management tools can store it or
process it efficiently
• Following are the types of Big Data:
• Structured
• Unstructured
• Semi-structured
• Agriculture date also consider as unstructured data
sets
• Apache Hadoop ecosystem is an open source
framework use for big data analysis:
• Data management
• Data access
• Data processing
• Data storage

5. SMART AGRICULTURE
• The term smart agriculture refers to the usage of
technologies like Internet of Things, sensors, location
systems, robots and artificial intelligence on your farm.
• The ultimate goal is increasing the quality and quantity
of the crops while optimizing the human labor used
• .Following process are using smart agriculture:
• Agriculture data collection
• Diagnostics
• Decision-making
• Actions
• Technologies that used in smart agriculture are:
• Greenhouses – climate management and control
• Sensors – for measure soil = PH level, water,
moisture and temperature management.
• LoRa WAN= mobile based long distance low
power consumption wireless networks.
• Analytics and optimization planforms = for data
processing and decision making

6. Why Smart Agriculture Needs
over Traditional farming?

7. Traditional Agriculture
• Weather. Harvesting success has always been
dependent on the weather. A good harvest
requires a balance of rainy and sunny days,
comfortable temperatures, and the avoidance of
weather-related disasters.
• Aging workforce. Farming is not a career path that
many young people pursue. Instead, they choose
more promising occupations.
• Routine and time-consuming tasks. Many of the
processes in agriculture are regular and manual.
• Lack of water.
• Unpredictability. The actual amount of food
harvested is challenging to predict because it
depends on many external factors.
• Global hunger. 10% of the global population still
suffers from starvation even though the
agricultural industry produces enough food to feed
every person on the planet.
Smart Agriculture
• Better weather prediction. weather data analytics
to build decision-making models for farmers based
on weather data, harvest specifics, and the current
health of the crop.
• Smart water and pesticide consumption. One of
the goals of digitization in agriculture is to help
farming businesses effectively use their resources
• Careful crop planting.
• Livestock tracking and management. Drones are
the primary tools that assist with this.
• Streamlined sales of farming products. Directly
connect with farmers and consumers .
• Supply chain optimization. Digital farming
solutions can help with the problem of food waste
by optimizing agriculture supply chains.
Over

8. RESEARCH METHODOLOGY
11%
19%
17%
29%
24%
Research Methodology
A B C D E
A. Historical Data : It consist unistructural data of soil and other
environments such as crop patterns, weather conditions,
climate condition and labor data.
B. Agricultural Equipment and sensor Data: It consist data from
remote sensing device such as soil temperature, farmers call.
C. Social and web-based data: it includes farmers and customers
feedback.
D. Publications: It includes agriculture research and agriculture
reference material such as text-based practice guidelines and
agriculture requirements
E. Business, Industries and External Data: The data from billing
and scheduling systems, agriculture departments, and other
agriculture equipment manufacturing companies.
Research Methodology Based On Following Parameters

9. RESULTS ANALYSIS
A. Farming Decision Support Big data analytics and ICT technology support to acquire, understand, categorize and discover information from
large amounts of data. Also, it can predict future or recommended decisions to farmers and vendors at the point of precision agriculture
B. Water Management Predictive data mining or analytic solutions over ICT can leverage water management and automatic irrigation
C. Increase Productivity Web ,and mobile-based applications visualize information from historical data, crop patterns and weather data.
D. Big data analytics and ICT solutions can also support agriculture equipment companies and departments to perform analysis over
agricultural growth and productivity, to support and identify future farming trends.
E. Agriculture Disaster Management Big data analytics and ICT applications can take initiatives such as real-time management in precision
agriculture, where it can mine knowledge from historical unstructured data, discover patterns to predict events that are harmful in
farming.
F. Patterns and decisions may help farmers in the disaster management in agriculture.
G. Policy, Financial and Administrative. The analysis supports policy makers, service providers, companies and government departments for
deciding future varieties, pesticides and fertilizers.

10. FUTURE APPLICATION
A.Easy farm monitoring: In the agriculture sector, factors affecting the
farming and production process can be monitored and collected, such
as soil moisture, air humidity, temperature, pH level, etc.
B.Tracking and Tracing: In order to meet the needs of consumers and
increase profit value, in the future, farms need to demonstrate that
products offered to the market are clean products and can be tracked
and traced conveniently, thereby enhancing the trust of consumers in
product safety and health-related issues.
C.Smart Precision Farming: The advent of the GPS (global positioning
system) has created breakthrough advances in many fields of science
and technology. The GPS provides the most important parameters for
locating a device, such as location and time. GPS systems have been
successfully deployed in many fields, such as smartphones, vehicles,
and IoT ecosystems
D.Greenhouse Production: A greenhouse consists of walls and a roof,
which are usually made from transparent materials, such as plastic or
glass. In a greenhouse, plants are grown in a controlled environment,
including controlling for moisture, nutrient ingredients of the soil,
light, temperature, etc. Figure: by 2025 -20.6 market cap.

11. IMPLEMENTATION
CHALLENGES :
A. Lack of Economic Efficiency:
1. The system initialization cost: The system initialization cost includes
hardware purchases (IoT devices, gateways, base station infrastructure)
2. The system operating cost: The system operating cost includes service
registration cost and the cost of labour to manage IoT devices.
Continue 

12. IMPLEMENTATION
CHALLENGES :
B. Technical Complexity:
1. Interference: IoT devices for smart agriculture can cause interference to different
network systems, especially some IoT networks using short spectrum bands LoRa WAN.
Interference can degrade system performance as well as reduce the reliability of IoT
ecosystems
2. Security and Privacy: problem, including the protection of data and systems
from attacks on the Internet. In regard to system security, IoT devices’ limited
capacity and ability led to complex encryption algorithms that are impossible to
implement on IoT devices.

13. CONCLUSION AND FUTURE
WORK
13
1. The IoT sensor device gathers real-time unstructured data of soil such as PH level, humidity,
nutrition level moisture level etc. and send to the big data server.
2. According to the big data analysis algorithm, this processes the data and provides the accurate
knowledge to the farmers and farming communities through the cloud-based service and mobile
apps application.
3. big data analysis techniques it deals with the concerned issues like how to increase the
agriculture productions and how to reduce the productions costs. Similarly, it addresses the time
management during the process, emphasizing the less use of chemicals and fertilizers. Moreover,
it takes the scientific efforts of agriculture to take over the trends of traditional and manual
techniques of doing agriculture and modeling it to smart agriculture model.
4. Model aims to reduce the manual and traditional efforts to 90% with the real-time information
basis using artificial intelligence and image processing technique through which problems can be
detected analyzing aroused images.

14. THANK YOU

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