AI.pptx

AI enabled Mobile Apps for Agro
Advisory and Extension Services
Dr. L. Muralikrishnan,
Scientist (Senior Scale),
Division of Agricultural Extension,
ICAR-IARI, New Delhi - 110012

What is AI?
• The term “Artificial Intelligence” was first introduced in the 1955 Dartmouth
Conference
• Artificial intelligence? is the science and engineering of making intelligent machines,
especially intelligent computer programs. It is related to the similar task of using
computers to understand human intelligence; Intelligence is the computational part of
the ability to achieve goals in the world.
• AI is one of the essential areas in computer science; it has penetrated in variety
of domains, such as education, healthcare, finance and manufacturing, because of
its’ nature to tackle problems that cannot be solved well by human
Views of AI fall into four categories:
Thinking humanly Thinking rationally
Acting humanly Acting rationally

AI Requires:
• AI Requires:
– Natural language
– Knowledge representation
– automated reasoning
– machine learning
– (vision, robotics.)
• Thinking humanly:
– Introspection, the general problem solver
– Cognitive sciences
• Thinking rationally:
– Logic
– Problems: how to represent and reason in a domain
• Acting rationally:
– Agents: Perceive and act

AI in Agriculture
• The application of AI in agriculture was first attempted by McKinion
and Lemmon in 1985 to create GOSSYM, a cotton crop simulation
model using Expert System to optimize cotton production under
the influence of irrigation, fertilization, weed control, climate and
other factors
• The globe is expected to reach 9.1 billion in 2050, 70 percent more
food needs to be produced with less available water and climate
change impacts (Indian vs Global food production systems)
• Decreasing manual labor, limited usable agronomic land and a
greater gap between total food produced and the world population
• AI in agriculture support for better yield with uniform plantation
crops resulting in better lifestyle for farmers

Uses of AI in Agriculture
• AI Plays very important role in soil management, weed
management, Pest and diseases management,
weather forecasting and market forecast supportive
intelligence to achieve more yields by selecting
appropriate crop varieties, adopting better practices in
soil and nutrient management, pest and disease
management, and help the government in determining
crop production estimates and predicting commodity
prices
• It also supportive in useful data analysis
Data – Information –Knowledge – Wisdom

AI enabled Mobile Apps for Agro
• Farming refers to a series of agricultural processes which involve
various day-to-day activities on the field, for example, sowing,
weeding, fertilizing, identify and correct plant diseases require
timely decisions
• Smart phones have become a useful tool in agriculture for its'
cost, accessibility and the mobile computing power for variety of
practical applications
• Also, the smart phones are nowadays equipped with various types
of physical sensors which make them a promising tool to assist
diverse farming tasks
• Recent advances in smart phone application development and an
increasing availability of smart phones helps to solve the issues in
an integrated manner through AI enabled Mobile Apps for Agro

Major Farming Applications
1. Disease Detection and Diagnosis. The AI enabled Mobile
Apps support to pest and disease detection/diagnosis in
farms which aids in plant disease identification process
• The system worked by capturing images of plant leaves
being investigated for diseases, then preprocessing those
images, and transmitting the processed images to remote
laboratories. The image preprocessing step was necessary
for saving transmission cost of sending diseased leaf images
to plant pathologists in remote laboratories. Leaf images
were segmented by a clustering algorithm into three
areas: background, non diseased portion of the leaf, and
diseased portion(s) of the leaf

Cont..
2.Fertilizer Recommendations: Crop-specific appropriate
fertilizer application is an important farming activity with a
potential decisions on which chemicals to apply and their
quantities need to be made by farmers
In this, a Smartphone-based color estimator dedicated for
respective crop leaves’ chlorophyll evaluation based
recommendation advise the farmers about the amounts of
nitrogen fertilizer application dosage
3. Soil Study: The mobile app based soil study support the
emerging precision agriculture centric soil data, Soil color
information, pH, soil carbon, organic matter, N,P,K and micro
nutrient information based a mathematical model based on
geographical advisories with the support of digital soil images

Cont..
4. Water Study: Water quality affects farming and agriculture; a
Smartphone application dedicated to encourage users to
submit information of water conditions, water level, water
clarity with the support of digital photographs for crop
specific recommendations for Crop Water Needs Estimation
based decisions in various conditions of crop types, season,
climate, and growth stages of crops
5. Crop Produce Readiness Analysis: An innovative use of smart
phone-based sensors is to determine ripeness of fruits.
Farmers could integrate the system into their farms by
separate fruits of different ripeness levels into piles before
sending them to markets.

Cont..
6. Pest and Disease Information : As mobile phones connected to the
Internet provide a way to quickly receive information and report
disease outbreaks, stakeholders (e.g., farmers, policy makers, and
field workers) can take appropriate actions to minimize the
damage. The application was implemented for mobile devices to be
able to display disease outbreak reports on a map and users can
also report or edit disease outbreaks. GPS is used to access current
location of the mobile phone to retrieve the nearby map and the
information that provides intelligent pest management solutions
7. Tools for Extension Workers: A different way that smart phones
can facilitate extension services is to provide a convenient tool for
extension workers. In land plot identification, conventionally,
technical staffs go to a farmer’s plot with multiple devices (e.g., a
GPS, a camera, and an electronic recorder to record data such as
the plot code, photograph identification, or date) and management
of the devices becomes a tedious task.

The Agricultural startups using AI to
overcome agricultural bottlenecks in
India
• Many Agricultural startups have taken on the challenge
themselves. They are using artificial intelligence to rescue
the agriculture industry to help increase crop yield, control
pests, monitor soil and growing conditions organize data for
farmers, reduce effort, and improve a wide range of
agriculture-related operations along the food supply chain

Cropin
• Founded in 2020 by Krishna Kumar and Kunal Prasad
• Cropin provides an AI-based intelligence mechanism to various
stakeholders in the agritech ecosystem for sustainable and
resilient agriculture.
• The Smart Farm Plus solution from this Bengaluru-based
startup integrates data from a variety of sources, thus digitizing
farm data.
• This includes third-party ERP solutions, manual input via the
SmartFarm app, earth observation and satellite-based
meteorological data, drones, and other IoT devices, using AI
and data analytics to support farmers' decision-making process
• Smart Farm Plus is an award-winning SaaS-based Farm
Management Platform to maximise per acre value of
agriculture farms with technology and intelligence. SmartFarm
Plus helps agriculture businesses redeﬁne field operations,
make smarter decisions on and off the field and enhance field
productivity

Cont..
• Agriculture is one of the major contributors to
GHGs; take, for example, agriculture alone
constitutes over 70 per cent of the methane
emission. Real-time data accessibility and
smart technologies can promote carbon
sequestration processes and help achieve
zero-carbon per farm, thereby minimising
greenhouse gas emissions.

Fasal
• Founded in 2018 by Shailendra Tiwari and Ananda Verma,
Fasal is a full-stack IoT SaaS platform for horticulture.
• With the help of on-farm sensors and machine learning
algorithms, the platform provides insights for the crop-
specific actionable insights, that too in vernacular languages
• The Bangalore-based startup uses on-farm data to provide a
14-day micro-climatic forecast advance to prepare in advance
for inconsistent weather.
• Similarly, the IoT devices placed in the soil measure the
moisture content of the soil, and the system monitors water
availability in the soil in real-time to ensure that the crop's
irrigation needs are satisfied precisely at all times, based on
the crop, its stage, and soil characteristics.

Intello Labs
• The startup, founded in 2016 by Milan Sharma employs new-
age tech, including AI, ML and computer vision, to tackle the
issue of wastage in India.
• India wastes around 18 per cent of its fruits and vegetable
production
• They generate product-based solutions for their clients using
the best analytics tools and methods – Deep Learning, AI,
Computer Vision, the Internet of Things (IoT), and Big Data.

DeHaat
• Founded in 2012 by IITalumni Amrendra Singh
• The Gurgaon-based startup DeHaat provides
farmers with access to more than 3,200
agricultural inputs, combined with AI-based
customized crop advisory on pest and disease
management, delivered via mobile app and call
centres.
• With real-time data, farmers can get weather
forecasts beforehand to make arrangements.
Moreover, the insights help in risk management
and mitigation as well.

References
• McCarthy J, Minsky ML, Rochester N, Shannon CE. A Proposal for the Dartmouth Summer Research Project on Artificial
Intelligence, 1955, 27(4):12-12.
• “2050: A Third More Mouths to Feed.” Food and Agriculture Organization of the United Nations, Food and Agriculture
Organization of the United Nations, 2020, www.fao.org/news/story/en/item/35571/icode/.
• Mckinion J M, Lemmon H. E. Expert systems for agriculture[J]. Computers & Electronics in Agriculture, 1985, 1(1):31-40.
• https://indiaai.gov.in/article/top-4-startups-using-ai-to-overcome-agricultural-bottlenecks-in-india
• More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
• S. Prasad, S. K. Peddoju, and D. Ghosh, “Energy efficient mobile vision system for plant leaf disease identification,” in Proceedings of
the IEEE Wireless Communications and Networking Conference (WCNC ’14), pp. 3314–3319, April 2014
• S. Sumriddetchkajorn, “How optics and photonics is simply applied in agriculture?” in International Conference on Photonics
Solutions, vol. 8883 of Proceedings of SPIE, June 2013
• S. Sumriddetchkajorn, “Mobile device-based optical instruments for agriculture,” in Sensing Technologies for Biomaterial, Food, and
Agriculture 2013, vol. 8881 of Proceedings of SPIE, The International Society for Optical Engineering, May 2013.
• Y. Intaravanne and S. Sumriddetchkajorn, “Baikhao (rice leaf) app: a mobile device-based application in analyzing the color level of
the rice leaf for nitrogen estimation,” in Optoelectronic Imaging and Multimedia Technology II, vol. 8558 of Proceedings of SPIE, The
International Society for Optical Engineering, November 2012
• M. Aitkenhead, D. Donnelly, M. Coull, and H. Black, “Esmart: environmental sensing for monitoring and advising in real-time,” IFIP
Advances in Information and Communication Technology, vol. 413, pp. 129–142, 2013
• R. Allen, L. Pereira, D. Raes, and M. Smith, “Crop evapotranspiration—guidelines for computing crop water requirements,” FAO
Irrigation and Drainage Paper 56, 1998
• R. C. L. Suen, Y. C. Ng, K. T. T. Chang, B. C. Y. Tan, and M. P.- H.Wan, “Interactive experiences designed for agricultural communities,”
in Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (CHI EA ’14), pp. 551– 554, May 2014
• F. J. Mesas-Carrascosa, I. L. Castillejo-Gonzalez, M. S. de ´ la Orden, and A. Garc´ıa-Ferrer, “Real-time mobile phone application to
support land policy,” Computers and Electronics in Agriculture, vol. 85, pp. 109–111, 2012
• https://www.digitalfrontiersdai.com/resources/plantix-a-mobile-based-crop-advisory-application/

AI.pptx

More Related Content

Similar to AI.pptx (20)

More from Murali Krishnan L (18)

Recently uploaded (20)

AI.pptx