Recent Scope for AI in the Food Production Industry Leading to the Fourth Industrial Revolution

Abstract

In today's situation, Artificial intelligence and computer vision collectively join together to analyze the big data obtained from predicted models. The role of AI in the agri-based food industry helps the stakeholders to access and monitor the supply chain. The phenomenon of applying AI and computer vision in the food industry would improve the entire operations. This research paper tries to provide an assisting model for farmers in food-processing and agriculture through the state-of-the-art method. Several concepts related to sustainability in food processing have been estimated through machine learning, and the deep learning model as a worldwide concept. The demand for the usage of AI and computer vision in the Ag-TECH industry has increased which impacts sustainable food production to feed the future. Certain implications have been suggested for real-time monitoring of the farming process, politics behind sustainable food production, and investment which is the main game-player in the present situation. The 4th Industrial Revolution [IR-4.0] was ushered in by the deployment of computer vision and AI in the food business, with computer vision robotics playing a crucial role in ensuring sustainable food production.

Full text

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Recent Scope for AI in the Food Production Industry Leading to the
Fourth Industrial Revolution
Elham Tahsin Yasin
Assistant Lecturer, Department of Information Technology, Noble Private Institute, Erbil, KRG,
Iraq. E-mail: ilham.tahsin.93@gmail.com
Nawroz I. Hamadamen
Assistant Lecturer, Department of Software and Informatics Engineering, College of Engineering,
Salahaddin University, Erbil, Kurdistan Region, Iraq.
E-mail: nawroz.hamadamen@su.edu.krd
Ganesh Babu Loganathan
Assistant Professor, Department of Mechatronics Engineering, Tishk International University,
Erbil, KRG, Iraq.
E-mail: ganesh.babu@tiu.edu.iq
Manikandan Ganesan
Lecturer, Department of Electromechanical Engineering, Faculty of Manufacturing, Institute of
Technology, Hawassa University, Hawassa, Ethiopia.
E-mail: mani301090@hu.edu.et
Received May 20, 2021; Accepted September 22, 2021
ISSN: 1735-188X
DOI: 10.14704/WEB/V18I2/WEB18375
Abstract
In today's situation, Artificial intelligence and computer vision collectively join together to
analyze the big data obtained from predicted models. The role of AI in the agri-based food
industry helps the stakeholders to access and monitor the supply chain. The phenomenon of
applying AI and computer vision in the food industry would improve the entire operations.
This research paper tries to provide an assisting model for farmers in food-processing and
agriculture through the state-of-the-art method. Several concepts related to sustainability in
food processing have been estimated through machine learning, and the deep learning model
as a worldwide concept. The demand for the usage of AI and computer vision in the
Ag-TECH industry has increased which impacts sustainable food production to feed the
future. Certain implications have been suggested for real-time monitoring of the farming
process, politics behind sustainable food production, and investment which is the main
game-player in the present situation. The 4th Industrial Revolution [IR-4.0] was ushered in by
the deployment of computer vision and AI in the food business, with computer vision robotics
playing a crucial role in ensuring sustainable food production.

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Keywords
Sustainability, supply chain, Artificial Intelligence, Computer Vision, IR-4.0.
Introduction
Artificial intelligence (AI) systems have piqued the interest of academics and practitioners
in the last 20 years. To begin with, various people have attempted to define AI. Some
authors define AI as a "machine's" ability to interpret the inputs offered by the
environment in a "smart" fashion or to better interpret the external factors through a
flexible setup (Kakani, 2020, Sun Q, 2019). AI denotes a new approach to create and
handle knowledge in a well-rethought marketing strategy (Di Vaio, 2020, Nicholas,
2019), such as the link between sustainability and innovation (Kumar, 2019). In greater
depth, innovation looks to be a commercial power. Indeed, breakthrough technology can
enable the implementation of business strategies that address the UN vision 2030.
As a result, an increasing number of businesses are developing AI-based agri-food
products capable of tackling many challenges while also conserving important resources
by minimizing damage to the environment. Business models that address ethical and
sustainable concerns are required for the implementation of AI technologies in the
management process. As a result, these business models provide a comparative benefit for
businesses without causing harm to the environment or community; as a result, the
business models could be described as sustainable, and therefore are referred to as
"sustainable business models" (SBMs) (Azadbakht, 2017, Barroca MJ, 2017).
The desire for new ways to reach and service people while maintaining prices low has
prompted the application of AI to improve customer happiness, supply chain
management, product quality, and lower costs in the food business, as well as other
industry sectors.
Novel food manufacturing and industrial processing procedures have been made possible
thanks to contemporary food sector developments (Babu 2019, Dr.Senthil Kumar, 2021).
Various sorts of food have been in demand for the past 5 decades, including some unusual
types like healthy ingredients, which have proven to be a key to a healthy lifestyle
(Ventures DB, 2017). To meet market demand and produce quickly, the food industry
developed a limited number of food processing procedures. Modern agricultural and food
production technology was employed and might be considered inventive pioneers in the
modernization of the food sector, before being supplanted by manufacturing facilities and
smart machines (Rezek, 2021). Will these developments be able to feed the world's

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rapidly rising population while avoiding the inevitable? It looks to be conceivable, given
the growth in supply and proportional increase in technological achievements (Ganesh,
2019, T. Muthuramalingam, 2019, Babu, 2021). During the previous century, the advent
of 4IR technologies like computer image robots and AI has resulted in a huge radical shift
in marketing tactics and investments (Dr. A. Senthil Kumar, 2020, B.K. Patle 2019). This
cutting-edge technology could be a useful tool in meeting future needs for a secure food
supply.
2. Artificial Intelligence is Divided into the Following Fields
Figure 1 Artificial intelligence fields
The following are the most important of them:
Machine Learning (ML)
It is a key study of statistical and algorithm frameworks used by computer networks to
efficiently complete activities without using explicit commands, instead of relying on
inference and patterns (Lele U, 2017). To recognize a simple item including an orange or
an apple, for instance. The goal is achieved not by directly stating the specifics and coding
it, but by displaying several alternative photographs of it to a youngster and thus allowing
the computer to design the measures to identify it like an orange or an apple (Dr. Qaysar
Salih Mahdi, 2021). Figure 1 depicts the domains in which AI is used in the food
processing industry.
Natural Language Processing (NLP)
NLP is a term that refers to software's autonomous processing of natural languages, such
as text and speech. It's a branch of computer science that studies how to program

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computers to handle and evaluate massive amounts of raw language processing
(Nabavi-Pelesaraei, 2021).
Vision
It's a branch of science that allows machines to see (Dr. Qaysar Salih Mahdi, 2021).
Machine vision uses digital signal processing, camera, and to gather and analog-to-digital
conversion to analyze visual data. It aims to automate actions that can be performed by
visual perception (Kumar, 2021).
Robotics
It is a branch of science and engineering concerned with the building, design,
management, and use of robotics, and also the control, visual perception, and data
processing provided by computer systems. These techniques are being used to create
machines that can take the place of people and mimic their actions (Kaab A, 2019).
Robots are frequently used to undertake activities that are hard or inconsistent for people
to complete.
Autonomous Vehicles
This vehicle is also known as a robot automobile or a self-driving car. It's a vehicle that
can recognize its environment and move with little or no assistance from humans (Vilkhu
K, 2008).
Artificial Intelligence uses in the Food Business
Selecting Raw Produce
One of the most critical difficulties that food production plants face is the uncertainty of
feedstock supplies. In food processing factories, manual sorting is utilized to filter and
segregate vegetables, leading to lower efficiency and greater prices (Marr B, 2017). AI,
which utilizes a combination of scanners, cameras, and machine learning to enable more
effective food sorting, can help food production companies gain significant productivity
in food classification (Nosratabadi 2020). Time-consuming operations for sorting local
produce, for instance, can be reduced by combining AI with sensor-based visual sorting
techniques, resulting in improved yields, better quality, and less trash (Marr B, 2017). AI
is being used to better adapt robots to manage a variety of item forms while lowering
waste and costs (Kakani, 202). Figure 2 depicts the potato sorting device.

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Figure 2 Sorting system for potatoes [16]
Figure 3 Sorting machine using artificial intelligence
Efficient Supply Chain Management
Supply chain control is a vital obligation for all food companies, given the growing
demand for transparency. The food firm uses food safety tracking and analyzing products
at every level of the supply chain to ensure conformity with consumer and industry
requirements to develop supply chains. To handle pricing and supplies, more precise
forecasting is needed (Jayashankar P, 2020). AI-based picture recognition solutions allow
for more effective and effective product sourcing. AI also aids in the effective and
efficient monitoring of products from producer to consumer, increasing consumer trust
(Rawat RM, 2021). The AI-based Sorting device for vegetation is shown in Figure. 3.

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Observance of Food Safety
AI-enabled sensors are used in food establishments to ensure that food employees follow
safety rules. Figure 4 illustrates how face recognition and object identification
technologies are used to determine if workers are practicing good personal hygiene as
required by the Food Safety Act. The screen images are retrieved for evaluation and can
be repaired in real-time if a violation is identified (Tsakanikas P, 2020). This method is
more than 89 percent effective.
Figure 4 AI enabled camera in a food facility
Equipment for Food Processing Cleaning
Existing cleaning methods are programmed to clean gadgets at predetermined periods.
This minimizes human involvement, minimizing the likelihood of food-borne viral
cross-infection. This technology, on the other hand, is designed for worst-case situations
and functions in the dark. Using AI-enabled technology (SOCIP), which uses infrared
waves and optical fluorescence scanning to evaluate food waste and microbiological
material in a piece of equipment and then improve the removal procedure (Tsakanikas P,
2020). As a result, there is a reduction in the amount of energy, time, and water used.
(Garton K 2020). The washing time has been reduced by half.
Anticipating Consumer Preferences
Food manufacturers employ artificial intelligence-based solutions to predict and analyze
their target customers' flavor choices, as well as anticipate their reactions to novel flavors.
Food makers will benefit from Artificial Intelligence-based data analytics in designing
new food items that are tightly linked with consumer preferences and tastes. In 2017, the
Kellogg Company introduced AI-enabled software that assists customers in deciding
which granola to use from a list of 50 components to create a personalized product

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(Kuo TC, 2018). The AI gives suggestions for what items to use in your granola and tells
you whether or not your items will work well together. Individuals aren't the only ones
who can benefit from artificial intelligence when making tiny amounts of granola. The
feedback mechanism created by the general information from flavor profiles, data on what
choices people make, and what combos people re-order refines what flavors people truly
like. This data source will most likely be highly beneficial to the parent firm when
selecting what new items to offer in its much bigger brands.
New Product Development
AI technology uses deep learning and statistical analytics to evaluate consumer taste
preferences and estimate how well they will react to novel flavors. Companies can
segment the data into demographic groups to help them build new products that appeal to
their particular audience's preferences (Bottani E, 2019). These might be used by
manufacturers to anticipate which products would be successful before they reach the
stores. Coca-Cola has installed self-service soft drink machines in several restaurants and
other locations, allowing customers to mix and match their drinks. Customers may
theoretically make hundreds of unique cocktails using this self-service equipment simply
varying the base drinks (Ganesh, 2019, Viswanathan M, 2020, Mohammed, 2020,
Dr. Idris Hadi Salih, 2020). Dozens of extra drink stations, each dispensing a variety of
beverages daily, generated a massive number of customer preference information, which
Coca-Cola is using ai to analyze. CHERRY SPRITE was the very first item to emerge
from this data since its artificial intelligence predicted that consumers would generate a
considerable quantity of cherry-flavored Sprite on their own and it would sell well.
Artificial Intelligence Front-End or Consumer Apps
1. Recommendation Systems
AI-based dietary research and recommendation engines can help consumers make
informed choices about what to consume and what not to eat by employing algorithms
that learn about the user's food demands and behavior (Ganesh, 2020, Suganthi K, 2020.
Ganesh, 2019).
2. Chatbots and Applications
By utilizing AI-based Virtual Associates, food establishments can ensure that customers
do not have to wait endlessly before submitting queries or modifying orders. The process

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has been simplified, which enhances the customer experience (Ganesh 2019, Qaysar Salih
Mahdi, 2021, Ellappan Mohan, 2020).
3. Self-Ordering AI-based Kiosks
As seen in figure 5, self-ordering devices driven by artificial intelligence can help
customers have a better service by reducing wait times and eliminating the need to queue
for checkout. Using embedded card scanners, such computers can take customer orders
and enable them to process transactions without the intervention of a human (Ganesh,
2021).
Figure 5 McDonald's Self Ordering Kiosk [give label]
4. Robots are getting some attention in restaurants, enhancing the speed and efficiency of
food manufacturing while also decreasing the time it requires to deliver meals.
Figure 6 Robots serving

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4.0 IR – Driven Agriculture or Food Industry
Computer Vision and AI Driven Food Industry
As an outcome of the current set of achievements in the AI sector, scientists and experts
predict that AI will evolve into key applications fueling many organizations by 2020. The
main cause for this is the rapid growth of digital information, which is expected to reach
44 trillion gigabytes per year by 2020. The 4.0 IR is on its approach through novel
approaches to solve existing challenges in numerous fields, thanks to this huge data and
trainable AI companies. The advent of industrial machines and equipment to build
constructions ushered in the Industrial Revolution (IR). With the invention of the radio,
electricity, and airplanes, the 2.0 IR began (Nicholas, 2019). The industry did not
encounter a serious setback till the early 1970s when electronics and the internet ushered
in the 3.0 IR by transforming the face of the globe through globalization and connection.
Thanks to recent breakthroughs in computer vision, data analytics, and AI, the 4.0 IR may
be witnessed in every field of production. The food industry was one of the businesses
that have recently seen a significant impact from AI on its procedures, tools, and
equipment. Crop farming, growth, processing, and production methods have all changed
as a result of the introduction of AI-driven procedures and equipment into farming and the
food processing industry. Computers may now do more than just show food photos; they
can also identify and reveal calorie info about that item. Taking things a step further,
IBM's AI Watson became the first AI chef in 2016 by proposing new and inventive meals
only by glancing at the components. IBM's Watson silenced renowned chefs with its key
capability of displaying variations in a dish with similar components. deep learning and
machine learning are AI techniques that have made it possible to process photos using a
computer's vision. Till 2012, image processing and computer vision were fields that
processed images and allowed computers to comprehend the contents of the image,
allowing them to make judgments. With the introduction of deep learning and machine
learning, image processing has been able to expand its capabilities beyond its previous
boundaries, reaching the pinnacle of technical innovation in tasks like detection, object
tracking. Data, photos, movies, linguistic sequences, and so on. DNN was shown to
produce superior results in terms of functionality in 2014, which drew attention to deeper
systems and learning methodologies. Several benchmarks containing massive amounts of
data for testing and training were created, eventually leading to a major contest to assess
the performance of DNN on these datasets.
Food Manufacturing Techniques based on Artificial Intelligence
The analysis of information on cuisine and food items prompted researchers to look into
the topic of food through AI lenses. In 2015, machines were clever enough to detect food

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from photographs provided, and in late 2016, MIT's AI was capable to estimate the
contents and nutritive benefits of food demonstrated it. This innovation took only a few
weeks to reach the general community as a mobile app. These AI solutions assist the food
sector in effectively promoting its items to the market via global food rising tactics and
marketing. Food manufacturing becomes more adaptable with equipment that can
differentiate between fundamental problems like oranges and apples and more
sophisticated duties like low saturated fats versus high unsaturated fats. Figure.7 A shows
the food control methods used at “Stemmer Imaging” for different applications, whereas
Figure 7 B shows the food tracking methods used at “Stemmer Imaging” for different
applications. 7 B shows how LeNet architecture is used to apply deep learning to food
categorization.
Figure 7 Computer vision and AI driven food industry (A) harvesting, picking, quality
control and sorting using vision algorithms, and (B) LeNet deep learning architecture for
food classification
Potential Prospective and Global Investments
Expanding demographics have a huge impact on things like government programs and
worldwide operations. The most pressing problem about this topic is balancing food

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production and consumption in developing nations with growing populations. Private and
governments investors are working to introduce AI and image processing innovations into
industries like food agriculture and industry to solve specific issues and maintain
productivity. The moderate increase in technological advancement lays the groundwork
for the country's better financial status, with this component as a foundation.
Countries like China and India, in particular, are implementing government programs to
increase food supply efficiency by utilizing technology like deep learning and information
analysis. Google and Microsoft, for example, are donating their technologies to these
nations and assisting in the formation of world economic sustainability. For instance,
Microsoft and the Indian government's ICRISAT collaborated to implement Microsoft
Cortana Smart Suite for agricultural information gathering and analysis using deep
learning techniques. The Indian government set up trial sites in thirteen districts to learn
about soil analysis labs, smart water systems, and IMOD techniques to promote farmers
via public-private partnerships and state expenditures.
Figure 8 AgTech investment increased in recent years
Many private companies are working to apply AI and image processing technology to a
task-specific agriculture benefit. In 2015 alone, the AgTech industry saw record-breaking
spending of over 13 billion US dollars (Figure. 8), prompting numerous analysts around
the world to predict AgTech as a significant movable component as a result of the 4IR.
To boost yields and achieve the objective of a stable food supply by 2050, AgTech
entrepreneurs are turning to task-specific AI and image solutions. AgTech startups
including ceres imaging, sky squirrel innovations, and blue river approaches use computer
vision techniques in the form of photo collection, spectrum signal processing, and
robotics. Sensor data may be a useful tool for analyzing farm characteristics, and
companies like sencrop, centaur analytics, and spensa solutions are using it to spot

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irregularities in agricultural output and energy supply. Startups like cibo, trace genomics,
myagdata, agrible, agrilyst, and benson hill biosystems, have used advances like plant
statistical analysis and pa to improve production and ensure sustainable food production.
Startups like aero farms, cropx aquaspy, alesca life, farm note, hydroponic information
systems, connecterra, vibrant farms, and innovative animal prognostics are using data
analytics and image processing methodologies to record, analyze, prototype, and
anticipate factors that can enhance yields in poultry and next farms for greenhouse
environment power guided by smart irrigation. Tables 1 show a complete list of AgTech
startups that use AI and image processing technology.
Table 1 Next-generation farms and animal information among the AgTech startups
Technology
Entry
Usage
Forth Coming Generation
farms
Aero-farms
Growing Fresh herbs, leafy greens,
without soil and sunlight
Upcoming Agtech Farms
Bright-farms
Greenhouse control farms
Animal Data
Farm-note
Herbs management system
Animal Husbandry data
Early Animal disease
diagnosis
Precise animal onsite and care disease
detection
Conclusions
The use of 4.0 industrial revolution innovations like ai and computer vision in farming
and the food business is discussed in this study. The current review, in particular, offers a
thorough comprehension of computer vision and intelligence methods that are applied to a
variety of agricultural apps, including food preparation, agriculture-based apps,
agriculture, plant statistical analysis, smart water management, and next-generation
agriculture. The research also focuses on the fundamental principle of using efficient 4IR
technologies to ensure that humanity has enough food by 2050 while staying ecologically
friendly. The importance of the AgriTech industry and advances based on AI and vision
capabilities were investigated using use-cases and appropriate information. The
agricultural food businesses that use computer vision and AI have been thoroughly
researched and classed according to their many applications. Aside from food and
agriculture-related firms, this article mentions a few others, including next-generation
farmland and animal information.
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