Page 150 - RUT Publisher

Life Science Research and Sustainable Development ISBN: 978-98-84663-33-9

supply chain, marketing and sales, and service management. Cloud computing makes this
integration possible. Eliminating the need of EDP unit in every factory, cloud computing can
reduce start-up costs of business.
Internet of Things (IoT)
Collecting small details of machines and process with identification is now possible with
rise of Internet of Things (IoT) making it a key component of smart industries. With IoT, machines
or environment in the factories are equipped with sensors with an identifiable address so that its
minute details can be recorded or exchanged at the central place or other web-enabled devices.
Using high-tech IoT devices in smart factories leads to higher productivity and improved
quality. Replacing manual inspection business models with AI-powered visual insights reduces
manufacturing errors and saves money and time. By applying machine learning algorithms,
manufacturers can detect errors immediately, rather than at later stages when repair work is more
expensive
AI and machine learning
Artificial Intelligence and machine learning provide machine ability to analyse the inputs
and generate optimal solutions for the given problem. Huge volume of data is generated through
different sensors including drone cameras and algorithms of machine learning given the power
to process these real time input to take much needed actions based on it.
Industries generate large volume of information at every step of business units. AI and
machine learning can help in providing inspection, predictability and decision making
automation of operations and business processes. For instance: Industrial machines are prone to
breaking down during the production process. Using data collected from these assets can help
businesses perform predictive maintenance based on machine learning algorithms, resulting in
more uptime and higher efficiency.
Data Science
Data science is the study of data to extract meaningful information for better planning and
implementations. It is a multidisciplinary approach that combines principles and practices from
the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze
large amounts of data.
Edge computing
The demands of real-time production operations mean that some data analysis must be
done at the “edge”—that is, where the data is created. This minimizes latency time from when
data is produced to when a response is required. For instance, the detection of a safety or quality
issue may require near-real-time action with the equipment. The time needed to send data to the
enterprise cloud and then back to the factory floor may be too lengthy and depends on the
reliability of the network. Using edge computing also means that data stays near its source,
reducing security risks.

Agriculture 4.0
Scientist have proposed the concept of site specific farming or precision farming running
on principal of site specific farming and gathering real time information or data about soil,
weather, crop etc. and analyse it for proper decision making. Agriculture 4.0 marks the extensive
use of technologies in managing the agricultural work efficiently. The technologies of industry
4.0 like Internet of Things is very good at real time data collection, Artificial Intelligence, machine
learning and data science make it possible to predict the trends and generate solutions or help in
decision making. These techniques can be incorporated at various stages of crop cultivation, crop
https://jesjalna.org/Zoology-Publications/index.html 142 Department of Zoology, J. E. S. College, Jalna