Artificial Intelligence and The Sustainable Development Goals (SDGs) Adoption of AI in Agriculture Industry.pptx

Artificial Intelligence and The Sustainable
Development Goals (SDGs): Adoption of AI in
Agriculture Industry
8 DECEMBER 2023

BIODATA EDUCATION:
1. University Technology Malaysia (UTM)
o Bachelor of Civil Engineering
o Master of Technology Management
2. University of Edinburgh Scotland
o PHD - Science and Technology
Get to know more about when you google “bcchew”
RESEARCH INTEREST:
o Green Technology Development & Deployment
o Sustainable Management
Associate Professor Ts. Dr. Chew Boon Cheong

Sustainable Development Goals (SDGs)
The Sustainable Development Goals (SDGs),
were adopted by the United Nations in 2015
as a universal call to action to end poverty,
protect the planet, and ensure that by 2030
all people enjoy peace and prosperity
The 17 SDGs are integrated — they recognize
that action in one area will affect outcomes
in others, and that development must
balance social, economic and environmental
sustainability
1
2

CATEGORIZING THE SDGS IN TERMS OF AI IMPACT

ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE
DEVELOPMENT GOALS (AI4SDGS)
Artificial intelligence (AI) is a
disruptive technology that can
revolutionize society and an
enabling technology that can
foster societal progress
AI development should
be used to promote the
achievement of the
global SDGs
AI research organizations,
universities, and companies all
have a responsibility to use AI
to enhance the long-term
growth of society, economy,
and environment
The global effort to AI to
assist sustainable
development, human rights,
and whole of humanity,
leaving no one behind

1
2
3
Artificial intelligence (AI) is the
development of computer systems that can
do tasks that typically require human
intelligence
Learning, reasoning, solving problems,
perceiving, comprehending language, and
even making decisions are all included in
these tasks
AI is an interdisciplinary science with
multiple approaches, but advancements in
machine learning and deep learning are
creating a paradigm shift in virtually every
sector of the tech industry
ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE IN AGRICULTURE
AI is widely employed in
several fields, and its
applications are progressing,
becoming more precise and
performant, including
manufacturing, healthcare,
banking and finance, aviation
and hospitality
AI in the agricultural sector
includes innovative
technologies such as field
sensors, drones, farm
management software tools,
automated machinery and
water and fertilizer
management solutions

END HUNGER, ACHIEVE FOOD SECURITY AND IMPROVED
NUTRITION AND PROMOTE SUSTAINABLE AGRICULTURE
UNIVERSAL ACCESS TO SAFE AND
NUTRITIOUS FOOD
END ALL FORMS OF MALNUTRITION
DOUBLE THE PRODUCTIVITY AND
INCOMES OF SMALL-SCALE FOOD
PRODUCERS
SUSTAINABLE FOOD PRODUCTION AND
RESILIENT AGRICULTURAL PRACTICES
MAINTAIN THE GENETIC DIVERSITY IN
FOOD PRODUCTION
INVEST IN RURAL INFRASTRUCTURE,
AGRICULTURAL RESEARCH,
TECHNOLOGY AND GENE BANKS
PREVENT AGRICULTURAL TRADE
RESTRICTIONS, MARKET DISTORTIONS
AND EXPORT SUBSIDIES
ENSURE STABLE FOOD COMMODITY
MARKETS AND TIMELY ACCESS TO
INFORMATION
THE TARGETS

FOOD AGRICULTURE
Agriculture is on the
frontlines of nearly all
urgent global challenges,
from hunger to climate
change to rising
inequality. Investment in
agriculture has an
important poverty-
reduction effect
especially amongst the
poorest people.
For the past five years,
hunger has been on the rise.
Every night, about 690
million people go to bed
hungry. Food production and
agriculture are important
because they support more
people than any other sector
on the planet.
Sustainable food
systems and
agricultural practices
can adapt, build
resilience, and
mitigate emissions.

AI IN AGRICULTURE CAN PLAY A STRATEGIC ROLE
At a global level, the agricultural sector has a value
of 3,6 trillion dollars, providing the 4% of the global
gross domestic product (GDP) with a stable measure
during the last twenty years (FAO, 2022).
In some developing countries, it accounts for more
than 25% of GDP (FAO, 2022).
Such a critical industry stands as a food and energy
base of the new economy, mainly because it ensures
food security (Magasumovna et al., 2017).

VARIOUS IMPLICIT PROBLEMS HAVE BEEN HISTORICALLY
CHALLENGING THE AGRICULTURAL SECTOR
THE NUMBER OF WORKERS WHICH IS SIGNIFICANTLY COLLAPSED
WITH A PROGRESSIVE DIFFICULT-TO-EMPLOY WORKFORCE
between 2000 and 2022, the global
workforce employed in agriculture collapsed
from 40% to 27%, representing a reduction
of 177 million people (FAO, 2022).
FOR EXAMPLE
01

VARIOUS IMPLICIT PROBLEMS HAVE BEEN HISTORICALLY
CHALLENGING THE AGRICULTURAL SECTOR
DECREASING PRODUCTIVITY CAUSED BY CLIMATE CHANGE AND
DESERTIFICATION
with a decline of 134 million
hectares of cultivated land between
2000 and 2020 (FAO, 2022).
FOR EXAMPLE
02

FOR THIS REASON…
achieving food security in a
sustainable way is one of the
objectives included in the
United Nations (UN) 2030
Sustainable Goals with the
Zero-Hunger program
(European Commission, 2017).
as a result, a growing need to modify
agricultural methods and available
technologies so that “maximum crops
can be attained and human effort can
be reduced” (Saad et al., 2021).
a country can be considered food
secure “if food is available,
accessible, nutritious and stable
across the other three dimensions”
(Musa and Basir, 2021).
the latest FAO World Food and
Agriculture – Statistical Yearbook
(2022), in 2021, 770 million people
were undernourished, with an
increment of 150 million from
2020 (Wijerathna-Yapa and
Pathirana, 2022).

APPLICATIONS IN AGRICULTURE
The topic of AI applications in agriculture is an opportunity to address some of the cited
problems creating new business scenarios in the agricultural sector
(Amoussohoui et al., 2022).
In the last years, the agricultural sector has started to integrate information and
communication technologies in traditional farming with the aim of improving crop yield
efficiency, reducing costs and optimizing process inputs with the usage of data.
Innovation technology, digitalization and AI could, therefore, represent some of the ways and
strategies to mitigate the above-mentioned issues, achieve sustainability goals and manage
the climate change challenge (DiVaio et al., 2020; Yela Aranega et al., 2022).

AGRICULTURAL SECTOR
CULTIVATION OF PLANTS ANIMAL PRODUCTION FISH FARMING

PROBLEMS TO SOLVE - OBJECTIVE TO ACHIEVE
Increase efficiency and
optimization maximizing
farm returns
Manage the environmental
impact and external changes
Predict and manage the farm
complexity
Feed the increasing global
population-food security

TECHNOLOGY USED
Decision Support System (DSS)
Artificial Intelligence and
Machine Learning
Big Data Analytics
Internet of Things (IOT) Drone Robots

TECHNOLOGY USED
Cloud Computing
Geographical Indication
System (GIS)
Biotechnology
Blockchain Autonomous Devices

Agronomic Planning and
Economic Applications
Precision Farming and
Agronomic Applications

Water Optimization and
Environmental
Management Applications
Food Supply Chain
Applications and Traceability

Platform business
model in the food
supply chain
Agritech 4.0 with
integrated smart
food supply chain
Supply chain
management 5.0
New information-
based system
based on
traceability
POSSIBILITY TO LEAD A NEW
BUSINESS MODEL

CONNECTS TO SUSTAINABILITY ISSUES
Reduce The Use Of
Pesticides, Heavy Metals
and Nitrates Which Pollute
Agricultural Soil and Water
Reduce The Consume and
Loss Of Water
Climate-oriented and
Ecologically Friendly
Applications
• Food Security In A
Sustainable Way
• Making Sustainable The
Ecological Impact Of
Food Production

ADVANTAGES AI IN AGRICULTURE
Efficiency Benefits and Productivity Increase
Food Safety and Easy Compliance
Organizational Advantages and Decision Support
Environmental Benefits

DISADVANTAGES AI IN AGRICULTURE
Difficult to Create A Unique System For Different Areas and Crops
Complexity to Realize
Environmental Impact in The Food Chain From Genetically Engineered Crops
Which Will Destroy The Actual Natural Habitat
Carbon Dioxide Emission As A Consequence Of Intensive Use of Energy
The System Cannot Work Without Power Supply

BARRIERS AI IN AGRICULTURE
Lack of Equipment, Internet Access,
Storage Capacity and
High-quality Data
Mismatch Between Applications and
Farmer Practical Needs
Farmers Lack of Technical
Knowledge About ICT and Other
Emerging Technologies
High Investment Costs and Low
Perceived Effectiveness
Lack of Integration and Complexity
of The Food Supply Chain
User Psychological Barriers to
Adoption
Data Control and Data Security
Large Energy Consumption and
Unsustainability

PRACTICAL IMPLICATIONS
Support Farmers In The
Decision-making Process
Support Everyday Farm
Operations Increasing
Efficiency & Effectiveness
Provide Farmers Useful
Forecasts To Manage The
Farm Unpredictability
Planning Their Activity
Provide Farmers New
Solutions With Integrated
Technologies

POLICY IMPLICATIONS
Governments should use the
agricultural data to improve
policy-making and decision-
making learning from data
Governments should create
advisory units to support the
farmers awareness about
complex technological tasks
Governments should
subscribe new investments
to enhance the
technological transition
Governments should support
the social innovation to
engage younger generations
to be more involved in the
honey and bee industry

THE MAIN AI-BASED
APPLICATIONS IN
AGRICULTURE

CONCLUSION
AI solutions agricultural
sector by offering sound opportunities to
farmers and entrepreneurs in the field to
support their decision-making process
and increase the farm’s profitability. Still,
fostering sustainability practices.

ARTIFICIAL INTELLIGENCE USES IN
SUSTAINABLE DEVELOPMENT GOALS (SDGs)
Poverty
Mapping in Big
Data Analytics
Source: World Bank Poverty and Inequality Platform (2022)
Unemployment Forecasts
Source: (AI: A Key Enabler for Sustainable Development Goals, 2018)

Analytics of Satellite
Images and Agro-
Meteorological Monitoring
Autonomous Mowing,
Picking And Harvesting,
Weed Control, Sorting and
Packaging, Etc

Predictive Healthcare -
Make More Effective and
Efficient Operational and
Clinical Decisions
Cognitive Healthcare -
Provides Decisions Based
on its Capacity to Gather
and Identify Data

Global Classroom
(Mixed Reality, Computer
Vision and Monitoring)
Machine Teaching
(An Emerging Sub-
Field of AI)

Monitoring and Track
Gender Bias
Women's Economic
Empowerment (Innovative
Skills Development and
Training Technologies)

Smart Water
Monitoring and
Management System
Smart Renewable
Energy Grid

Improve Job Security
(Utilizing AI to Automate
Mundane Tasks)
AI-Powered Training
Solutions

Industry 4.0
(Smart Factory)
Robotic Assistive
System

Smart City
(Municipality That
Uses ICT)
Energy
Consumption
Monitor

Smart Disaster
Response
Capture Information
for Evaluation from
Camera Systems or
Marine Vehicles

A Transparent Digital Earth
(tackling natural disasters, air
pollution, illegal deforestation,
fishing, poaching, etc)
Smart Agriculture and
Food Systems

Reduce the amount of
time and resources
required for data
gathering, analysis, and
option creation, and
allocate those resources
instead to more
challenging tasks like
strategic decision-making,
dialogue, negotiation, and
trust-building
Algorithms driven by
AI can sift through
enormous volumes of
data to find possible
partners that share
their values,
objectives, and vision

REFERENCES
AI-Ethics frameworks with the UN SDGs - Google Search. (n.d.). Www.google.com. Retrieved November 28,
2023, from https://www.google.com/search?q=AI-
Ethics+frameworks+with+the+UN+SDGs&sca_esv=585931027&rlz=1C1CHBD_enMY916MY916&tbm
=isch&sxsrf=AM9HkKkcFwYUAnhapb4vF0jUmqlLIqNTjA:1701179068316&source=lnms&sa=X&ved=
2ahUKEwisxsPw6eaCAxV4aGwGHZtPC3EQ_AUoAnoECAEQBA&biw=1229&bih=591&dpr=1.56#imgr
c=JytYDzzkk0PRgM
AI: A Key Enabler for Sustainable Development Goals. (2018, April 27). Www.slideshare.net.
https://www.slideshare.net/AlaaKhamis/ai-a-key-enabler-for-sustainable-development-goals-
95241642

REFERENCES
AI4SDGs-Research-Program-2020. (2020). Ai-For-Sdgs.academy. https://www.ai-for-
sdgs.academy/AI4SDGs-Research-Program-2020
Artificial Intelligence for Sustainable Development Goals (AI4SDGs) Research Program. (2020). Ai-For-
Sdgs.academy. https://www.ai-for-sdgs.academy/ai4sdgs-research-program
Burns, E. (2021). What Is Machine Learning and Why Is It Important? SearchEnterpriseAI.
https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML
IBM. (2023). What is Machine Learning? IBM. https://www.ibm.com/topics/machine-learning
Nasir, O., Javed, R. T., Gupta, S., Vinuesa, R., & Qadir, J. (2023). Artificial intelligence and sustainable
development goals nexus via four vantage points. Technology in Society, 72, 102171.
https://doi.org/10.1016/j.techsoc.2022.102171

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