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ICAR initiatives on Application of Artificial Intelligence and Internet of Things in Agriculture

The document outlines initiatives by ICAR-National Academy of Agricultural Research Management (NAARM) to integrate AI and IoT into agriculture, detailing various training programs, workshops, and research findings from 2018 to 2019. It presents survey results highlighting awareness and application of AI and IoT among agricultural researchers, along with recommendations for incorporating these technologies in crop and natural resource management. The document also discusses platforms, researchable issues, and necessary integrations for effective deployment of AI and IoT in the agricultural sector.

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ICAR Initiatives on Application of AI and IoT in Agriculture SK Soam ICAR- National Academy of Agricultural Research Management, Hyderabad
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
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)
Recent trends: AI in Agriculture (Unpublished Results & Copyrights : Dr S. K. Soam)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)
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)
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)
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)
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
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
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
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
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%
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.