The National Academy of Agricultural Research Management, Hyderabad, India conducted several workshops and developed policy brief as part of ICAR initiatives on Application of Artificial Intelligence and Internet of Things in Agriculture

1. ICAR Initiatives on Application of
AI and IoT in Agriculture
SK Soam
ICAR- National Academy of Agricultural Research Management,
Hyderabad

2. AI & IoT Activities at NAARM
• January 2018: Inclusion of two sessions in training
programme
• March-April 2018: Questionnaire survey with about 200
trainees at NAARM
• May 2018: Literature and databases search; Observations
at Australia/ Newzealand
• 1-2June 2018: Conducting a Two-day National Dialogue at
NAARM, Hyderabad
• July 2018: Writing policy brief
• 30-31 July 2018: ICAR National Workshop on AI at New
Delhi by NAARM, IASRI ,IIWM
• April 2019: Proceedings and Recommendations of
National workshop on Artificial Intelligence in Agriculture

3. Recent trends: web of science
[Source: NAARM Policy Brief, July 2018 No (2)]
Total Publications by Year
for Keyword: (AI, IoT in agriculture)
Sum of Times Cited by Year
for Keyword: : (AI, IoT in agriculture)

4. Recent trends: AI in Agriculture
(Unpublished Results & Copyrights : Dr S. K. Soam)
198 186
152
98
113
131
92
139 132
148 152
182
212 212 203
237
330
359 367
415
512
621
874
777
0
100
200
300
400
500
600
700
800
900
1000
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
NumberofPublications
Year
Artificial Intelligence in Agriculture: Research Trends
(Source: Science Direct)

5. Recent trends: IoT in Agriculture
Source: Proceedings of National Workshop on AI in Agriculture, ICAR-NAARM,
ISBN: 9788193378144)
50
83 79
56
94
147
129
178
163
197
160
195
234
216
239
258 261
340 350
433
526
655
828
731
0
100
200
300
400
500
600
700
800
900
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
NumberofPublications
Year
Internet of Things in Agriculture: Research Trends
(Source: Science Direct)

6. Recent trends: AI in Agriculture
(Source: Proceedings of National Workshop on AI in Agriculture, ICAR-NAARM,
ISBN: 9788193378144)
5,4201,189
1,166
573
315
226
138
113
98
93
92
50
35
34
2
2
2
1
1
1
1
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500
Research articles
Book chapters
Other
Review articles
Discussion
Encyclopedia
Conference abstracts
News
Editorials
Book reviews
Short communications
Conference info
Correspondence
Mini reviews
Errata
Patent reports
Product reviews
Case reports
Data articles
Practice guidelines
Software publications
No. of Articles
TypeofArticles Article Types in Science Direct
(Key Word: Artificial Intelligence in Agriculture)

7. Recent trends: IoT in Agriculture
Source: Proceedings of National Workshop on AI in Agriculture, ICAR-NAARM,
ISBN: 9788193378144)
4,103
1,353
420
236
203
82
66
51
48
47
43
24
15
6
4
1
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500
Research articles
Book chapters
Review articles
Encyclopedia
Other
Editorials
Discussion
Short communications
News
Conference abstracts
Book reviews
Mini reviews
Conference info
Correspondence
Software publications
Product reviews
No. of Articles
TypeofArticles
Article Types in Science Direct
(Key Word: Internet of things in Agriculture)

8. Questionnaire survey results
[Source: NAARM Policy Brief, July 2018 No (2)]
72%
34%
28%
66%
0%
10%
20%
30%
40%
50%
60%
70%
80%
AWARENESS ABOUT AI DOMAIN KNOWLEDGE ABOUT AI
Application of Artificial Intelligence (AI) in Agriculture
Yes
No

9. Questionnaire survey results
[Source: NAARM Policy Brief, July 2018 No (2)]
60%
28%
40%
72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
AWARENESS ABOUT IOT DOMAIN KNOWLEDGE ABOUT IOT
Application of Internet of Things (IoT) in Agriculture
Yes No

10. Questionnaire survey results
[Source: NAARM Policy Brief, July 2018 No (2)]
37%
31%
54%
28%
0%
10%
20%
30%
40%
50%
60%
AI IoT
AWARENESS ABOUT AI & IOT
N=155( NEWL E Y RECRU IT E D = 69, MIDDLE LEVEL= 86 )
Newly Recruited
Middle Level
Awareness of Agricultural Researchers about AI & IoT

11. Questionnaire survey results
(Copyright: only for self consumption)
23%
31%
10%
28%
0%
5%
10%
15%
20%
25%
30%
35%
AI IoT
DOMAIN KNOWLEDGE OF AI & IOT IN AGRICULTURE
N=155(NEWLEY RECRUITED= 69, MIDDLE LEVEL= 86)
Newly Recruited
Middle Level
Domain Knowledge of Agricultural Researchers about Application of AI & IoT in Agriculture

12. Questionnaire survey results
(Source: Proceedings of National Workshop on AI in Agriculture,
ICAR-NAARM, ISBN: 9788193378144)
Type of Service No of Ideas %
Prescriptive Service 22 23%
Advisory Service 21 22%
Prediction Service 36 37%
Diagnostic Service 18 19%

13. Platforms for AI & IoT (software)
Name Application
Plantix Plantix is your mobile diagnostic tool for fruits, vegetables and field crops. Powered
AI.
PlantNet
Plant Identification
It helps identifying plant species from photographs, through a visual recognition
software using AI.
PictureThis
Plant Identification
It to identify any plants and flowers using advanced AI tools (visual recognition and
deep learning technologies ).
Merlin Bird ID Merlin Photo ID uses computer vision and deep learning technology to identify birds
in photos. Merlin's powerful AI will suggest an identification almost instantly.
iNaturalist Naturalist helps you identify the plants and animals around you using AI
ripeSense Intelligent sensor label that changes colour to indicate the ripeness of fruit. Powered
by IoT and AI
CCMobile App
(ConnectedCrops)
Powered by IoT platform, the app allows you to get the most out of precision
farming. Get the latest sensor readings and events anytime, anywhere, compare
station readings, set up alerts and the update frequency of sensor readings, etc.
Mainly used for disease identification using image processing techniques.

14. Platforms for AI & IoT
Name Application
Stellapps
(SmartFarms,
smartAMCU, ConTrak,
AgRupay, MooKare)
Provides Animal recording, productivity and peak-yield management, breeding,
preventive health care, fodder management and veterinary care, simplifies milk
procurement for dairy farmers, milk collection centres, and societies in general.
powered by IoT & AI.
CropIN CropIn is an intuitive, intelligent, self-evolving system that delivers future-ready
farming solutions to the entire agricultural sector. Digitizing every farm with
capabilities of live reporting, analysis, interpretation and insights
YUKTIX Provides Sensor based smart solutions for environment sensing, agriculture and smart
infrastructure that has the potential to impact millions of lives across the world using
IoT and AI. Crop Disease Prevention, Smart Greenhouse, Post Harvest Monitoring
and Remote Management.
ROXAN ROXAN has introduced farmers to a new and innovative approach to EID Tagging,
Reading and Data Management using IoT and AI technologies.
Agribotix Agribotix provides the agricultural intelligence services that provides fully supported,
user-friendly, drone-enabled technologies and services developed exclusively for
agriculture.
Daisy Daisy provides automatic plant watering solution. It uses daisy watering device for
automatic watering utility for indoor and outdoor plants.

15. Researchable issues in Agriculture
(Source: Proceedings of National Workshop on AI in Agriculture,
ICAR-NAARM, ISBN: 9788193378144)
 Real time soil moisture monitoring, pest & disease identification, surveillance & forecasting,
crop area estimation through remote sensing, yield estimation of field and horticultural crops,
livestock management, predictive maintenance of farm machinery, crop stress and field
validation for confirmation of crop health, etc.
 Block chain technology for tracking and tracing of agricultural commodities from farmers to
consumers. IoT applications have wide scope in managing cold storage and supply chain
logistics for monitoring and product quality assurance.
 Developing Robotic system for various farm operations such as harvesting of cotton,
sugarcane bud cutting and planting, fertigation, irrigation, Ultrasonic sensor based spraying
system may be developed as field prototype and for commercial production.
 Developing IoT based Wireless Sensor Network (WSN) for pest and disease forewarning and
irrigation scheduling by monitoring various parameters like temperature, relative humidity
etc.
 Developing models for applicability at the farm level efficient drainage affluent system, root
zone salinity, crop evapo-transpiration, hydrology and irrigation scheduling in green houses.
 Creating term banks (Terminologies/Vocabularies/Dictionaries) for agriculture domain and
sub-domains in Indian languages; working for development of phonetic dictionary of
agriculture domain terms for adaptation of speech based systems.

16. Researchable issues in Agriculture
 Digital grading of food grains, fruits, vegetables, spices etc. The devices will be useful if
made at low cost and could be implemented with minimum infrastructural requirement
covering wide geographical area
 Developing algorithms / process / models which have direct application of modern tools of AI
and machine learning in agriculture.
 Custom hiring applications of farm equipment through mobile app (like Uber for Taxi) for
enabling small farmers to make the proper use of equipment on sharing basis rather than
purchasing the same.
 Exploring various problem domains of integrated water management system like:
(i) Estimation of actual evapotranspiration and root zone soil moisture from satellite image
derived hydrological parameters,
(ii) Delineation of waterlogged/ salt affected cropped land and economic losses
(iii) Prediction of farmers' decision making, its impact on crop yield and improving land and
water productivity in saline irrigated environment.
 Listing of all scattered knowledge resources and establishing a framework for accumulating
these resources in incremental fashion.

17. Recommendations from National Dialogue
Application in Crop Sciences/ NRM/ Animal
Sciences[Source: NAARM Policy Brief, July 2018 No (2)]
Crop Sciences
The two important input factors made available:
i) Good Quality Data
 Collection and quality analysis of data: Data should be collected in coordination with computer
applications experts, keeping in view of crop/expert models.
 For keeping pace with the dynamic environmental conditions, data should be collected on real time
basis.
 Data generated through AICRP trails and satellite data should be optimized.
 Integration and validation of existing data.
 Digital herbarium/databases like germplasm, climate, field data etc. need to be integrated.
 Forecasting and crop biomass estimation
ii) Sensors
 Need based sensors, kind of material that remains sensitive for many years, efficient and cost
effective. Safe disposal of field sensors.
 Handy neuro-chips for farmers, a kind of Fit bit.
 Application of Drones.
 Development of electronic eye for several purposes.

18. Recommendations from National Dialogue
Application in Crop Sciences/ NRM/ Animal
Sciences
Natural Resource Management (NRM)
 ICAR should consider taking a mega project on development of AI tools for validation and
integration of available data on weather, soil and other natural resources.
 Soil nutrient mapping using appropriate IoT and AI should be given priority for conservation and
sustainable utilization of resources.
 Development and adaptation of indigenous sensors for monitoring of natural resources like soil
and water should have sufficient focus so that cost effective and robust sensors are available for
deployment of IoT.
 UAV based tools, protocols and gadgets need to be developed for data collection, modelling,
decision making and input application.
 A digital database on different crops, diseases, insects, pests, animal breeds and their diseases, etc.
should be developed so that image processing, audio processing and other such tools in
conjunction with AI could be deployed.

19. Recommendations from National Dialogue
Application in Crop Sciences/ NRM/ Animal
Sciences
Animal & Fisheries Sciences
• Animal tracking system; health monitoring system.
• Traceability of animal products
• Quality check system at primary milk cooperative society level.
• Feed/input management & monitoring.
• Water quality management.
• Accuracy of sensors (calibration for quality data)
• Biomass estimation.

20. Recommendations from National Dialogue
Integrations
Integrations
• National level nodal point should be created having linkages with various institutions.
• Opportunity of sustainable continuum of biologists and electronics.
• Linkages with private industry for customization of sensors at large scale.
• Integration with CDAC, IITs, IoT hardware suppliers and other private players in the ecosystem is
essential.
• Develop AI and IoT as area of specialization.
• Different AICRPs involved in collection and warehousing of such data, ICAR institutes such as
NBSSLUP, CRIDA, IASRI, IARI, state departments and NRSA could
• collaborate for NRM data.
• CIAE, CSWCRI, IISS should take lead in association with IITs, Universities, CSIR and private for
sensor development and manufacturer.
• ICAR-CIAE along with IASRI in association with various commodity institutes could take lead in
digital database development.
• All animal and fishery institutes need to network with CDAC and IITs.
• IoT hardware suppliers.
• Collaboration with private business enterprises.

21. Recommendations from National Dialogue
Strategies
Strategies
• Policy support for developing competent skill in these area; training to young and middle level
scientists in AI and IoT in identified institutions abroad.
• ICAR-NAARM need to act as nodal point for providing capacity building programs in AI/IoT to the
NARES which can be extended to industry and agri start-ups as well.
• Access to data from AICRPs/KRISHI and other sources through sharing of APIs enabling real time
access thus creating a central data warehouse.
• Incentives for students to pursue courses of study that will allow them to create the next generation
of AI.
• Encourage investment in projects / infrastructure to support and deliver AI based services, and
partnering with private industry need to be promoted.
• Developing integrated flagship programs such as 'Niche Area Excellence' in identified consortia of
ICAR institutions.
• ICAR need to play much bigger supportive role for implementing AI and wide spread of AI
applications to farmers by strengthening linkages with private industries. The kind of hand holding
and support is required for private businesses in terms of capacity building and domain consultancy.

22. Experiences from Australia
Australian Centre for Field Robotics,
University of Sydney, Sydney
• Ladybird for horticultural
operations
• Swagbot for soil pasture
quality maintenance
• RIPPA for vegetable
production activities
• Di wheel for small farming
operations
• Farm hand and Agbot
• Precision agriculture
• Climate studies
Institute for the Environment,
Western Sydney University

23. Thanks
soam@naarm.org.in / sudhir.soam@icar.gov.in
9440945340
Citations:
Soam SK, M. Balakrishnan, VV Sumanthkumar and Ch. Srinivasa Rao (2018).
Artificial Intelligence and Internet of Things: Implications for Human Resources
in Indian NARES. NAARM Policy Brief, July 2018 No (2). ICAR- National Academy
of Agricultural Research Management, Hyderabad.
L.M. Bhar, S.K. Ambast, S.K. Soam and Ch. Srinivasa Rao (2019). Status and
Prospects of Artificial Intelligence in Agriculture. Proceedings and
Recommendations of National workshop on Artificial Intelligence in Agriculture
held during July 30-31, 2018 at NASC Complex, New Delhi, pp 1-92. ISBN 978-
81-933781-4-4.