This document discusses using data analytics in agriculture. It notes that agricultural data comes in many forms, including structured and unstructured data from public and private sources on topics like climate, soil, crops, diseases, and market prices. Challenges include turning this data into actionable outcomes like predictive analytics and apps that link farmers to service providers. Existing mobile and call center approaches are limited and could be improved with personalized advisories, two-way voice and image exchanges, and up-to-date farmer profiles. Example scenarios where data analytics could help include market intelligence, process optimization, and integrated services that provide advice, financing, inputs and sales channels on a single platform. An interactive system is proposed to disseminate personalized advisories

1. Data Analytics for
Agriculture
PRSENTED BY:-
ARCHIT DHAMA-DSA
2101331540014

2. Data: Open Data ++
• Agricultural Data
• Structure & Unstructured
• Public & Private
• Gross and Precision
• Numerical, Categorical, Textual, Spatial
• Images
• Variety and Velocity
• Climate/Weather, Soil
• Produce
• Crop, horticulture, livestock, fisheries
• Disease, Stress factors, fertiliser/chemical usage
• Mechanisation, Power, Irrigation
• Market Prices (available as open data)

3. Challenges
• Data to Actionable Outcome
• Actionable outcome:
• Bridge between Service Providers and Service Consumers
• Apps to provide link
• Big data analytics
• Algorithms as Service
• Structured between Cloud and End-user delivery platform like
mobile
• Service on demand
• Predictive Analytics
• Example -Market Intelligence
• Diagnostic Analytics
• Example- Process Intelligence

4. DeliveryModalities:AStudy
Way Forward
Gaps Identified
Mobile Based Initiatives
Text SMS
Generic Information Delivered
Language / Literacy Barrier
Limited records of the farmers & their farming
details
No direct interaction with expert (for push
based services)
Voice Calls
Largely Push based services and information
delivery at undesired time
No direct interaction with the expert (for push
based services)
Call Centre / IVRS
Information provided (at both ends) on voice
alone is not always complete
Limited records / database of the farmers
No / very limited follow up of services
Requirement for farmer’s specific information
Requirement for voice & Image based
information Exchange
Requirement for Updated info
Requirement for Personalized Advisory
Requirement of right information at right time
as time desired by farmer
Requirement for Personalized Advisory
Requirement for other modes of information
exchange for better understanding
Requirement for complete & updated database
- Farm & Farmers
Requirement for Experts field visit and other
feedback mechanisms

5. Example Scenarios

6. Agri-business
• Collection of data on supply, demand and price trends of
selected agricultural products in green market, wholesale
markets, livestock markets, slaughterhouses and silos
• Analysis of the collected data and dissemination of market
information on selected agricultural products (fruits,
vegetables, livestock, cereals, feed and inputs)
• Enable farmers, creators of the agrarian policy, enterprises
and other participants in making sound business decisions
regarding production and marketing of agricultural
products

7. Agro-Advisory
• Information need for farmer
• Information requirement for input provider
• Forecast demand correctly, to forecast crop
yielding, to determine land area and usage
• Real time online input for problems and difficulty
• Sharing of mechanised appliances
• Farmers can plan, run and analyse their entire
farming operation through the entire farming
cycle as efficiently as possible
• Input providers can devise adaptive strategy for
profitability and marketing
• Responsible & Clean technology

8. Integrated Services
Customised agricultural
advice
access to credit and
insurance products
access agricultural inputs
and a channel for selling their
final produce –
all on an integrated
platform
13
20
/
11
8/
8
Requirement Driven Model
Data
Anaalytics
Service
Knowledge
Service

9. IASP – Proposed Structure

10. Interactive Information Dissemination System for farmers :
Pull and Push based system : Data can be transmitted through voice, text,
images and videos from both end (farmers to expert and back).
Currently available in Hindi, Telugu &
Multi-platform: Web, Mobile & IVRS
Multilingual Web Portal and IVRS :
English
Personalized Agro Advisory Based on 'Farm & Farmer Profile’
Integrated Multi-platform
Data Analytics Delivery

11. ARCHIT DHAMA
from Noida Institute of Engineering & Technology has successfully completed a 6-week online training on
Data Science. The training consisted of Introduction to Data Science, Python for Data Science, Understanding
the Statistics for Data Science, Predictive Modeling and Basics of Machine Learning, and The Final Project
modules.
In the final assessment, ARCHIT scored 82% marks.
We wish ARCHIT all the best for future endeavours.
Date of certification: 2022-10-08 Certificate no. : 3256003C-F2D2-DC29-3561-7AB1216623FF
For certificate authentication, please visit https://trainings.internshala.com/verify_certificate

12. THANK YOU