SOCIAL MEDIA AND ARTIFICIAL INTELLIGENCE IN AGRICULTURAL COMMUNICATION IN INDIA: A SYSTEMATIC REVIEW AND BIBLIOMETRIC ANALYSIS, 2010–2025
DOI:
https://doi.org/10.29121/shodhai.v3.i3.2026.120Keywords:
Social Media, Artificial Intelligence, Agricultural Communication, Kisan Call Centre, Systematic Review, Bibliometric AnalysisAbstract
Social media and artificial intelligence (AI) are changing the way agricultural information is shared in India; however, research on these two tools of communication has not been reviewed together. The present review examined the publication profile of Scopus-indexed research on social media and AI in Indian agricultural communication, the way this research has studied the two tools, and what the evidence shows about them. Following PRISMA 2020, Scopus and OpenAlex were searched for the period 2010–2025, and every eligible study was checked for Scopus indexing one by one. Out of 2,735 records, 33 studies were included, coded by tool, research design, theme and actor, and synthesised narratively. The research is very recent, as 82% of the studies appeared in 2021–2025. Sixteen studies examined social media, mainly WhatsApp, 14 examined AI, mainly machine-learning analysis of Kisan Call Centre queries, and three applied AI to agricultural tweets. Content and data analytics was the most common design (52%), and none of the studies was a randomised trial. Scopus indexed 54% of the eligible AI studies but only 14% of the eligible social media studies. Farmers preferred WhatsApp and familiar tools and created as well as received content, whereas AI was mostly used to analyse farmers' questions and was rarely tested with farmers. The review concludes that the evidence is recent, descriptive and thin on effects, and that future research should test both tools with farmers in the field.
References
Aker, J. C. (2011). Dial "A" for Agriculture: A Review of Information and Communication Technologies for Agricultural Extension in Developing Countries. Agricultural Economics, 42(6), 631-647. https://doi.org/10.1111/j.1574-0862.2011.00545.x
Amalan, A. A., and Aram, I. A. (2025). Artificial Intelligence Adoption in Non-Chemical Agriculture: An Integrated Mechanism for Sustainable Practices. Sustainability, 17(19), Article 8865. https://doi.org/10.3390/su17198865
Babu, J. M., Yadav, A., and Shivnani, T. (2025). A Step Toward Digital Inclusion in Agriculture: Managing ICT Capacity-Building and Measuring Outcomes Among Smallholder Farmers. Indian Journal of Finance, 19(8), 68-88. https://doi.org/10.17010/ijf/2025/v19i8/175234
Burman, R. R., Mahra, G. S., Singh, A. K., Mallick, S., Anand, A., Vashisth, A., Mishra, G., Shekhawat, K., Somvanshi, V., Rudra, S. G., Sangwan, S., Kumar, B., and Das, A. K. (2022). 'Pusa Samachar': An Innovative Multimedia-Based Extension Advisory Model. Current Science, 123(4), 574-582. https://doi.org/10.18520/cs/v123/i4/574-582
Coggins, S., McCampbell, M., Sharma, A., Sharma, R., Haefele, S. M., Karki, E., Hetherington, J., Smith, J., and Brown, B. (2022). How Have Smallholder Farmers Used Digital Extension Tools? Developer and User Voices From Sub-Saharan Africa, South Asia and Southeast Asia. Global Food Security, 32, Article 100577. https://doi.org/10.1016/j.gfs.2021.100577
Coggins, S., Munshi, S., Smith, J., Yadav, A. K., Poonia, S. P., Patil, S., Singh, N. K., Sawarn, A., Ireland, D. C., McDonald, A. J., Singh, D. K., Sherpa, S. R., and Craufurd, P. (2025). How Do Chat Apps Support the Use of Farming Videos in Agricultural Extension: A Case Study From Bihar, India. NJAS: Impact in Agricultural and Life Sciences, 97(1), Article 2420803. https://doi.org/10.1080/27685241.2024.2420803
Cole, S. A., and Fernando, A. N. (2021). 'Mobile'izing Agricultural Advice: Technology Adoption, Diffusion and Sustainability. The Economic Journal, 131(633), 192-219. https://doi.org/10.1093/ej/ueaa084
Das, P., and Pradip, D. (2021). Usability and Effectiveness of New Media in Agricultural Learning and Development: A Case Study on the Southern States of India. Journal of Social Marketing, 11(4), 357-377. https://doi.org/10.1108/JSOCM-11-2019-0203
Das, S. (2021). Influence of Social Media on Agricultural Advisory Service: Analytical Study on the Agricultural Extension Specialists of Uttar Banga Krishi Vishwavidyalaya, West Bengal. Library Philosophy and Practice, 1-17.
Department of Agriculture, Cooperation and Farmers Welfare. (2019). All India Report on Number and Area of Operational Holdings: Agriculture Census 2015-16. Ministry of Agriculture and Farmers Welfare, Government of India.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., and Lim, W. M. (2021). How to Conduct a Bibliometric Analysis: An Overview and Guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Fabregas, R., Kremer, M., and Schilbach, F. (2019). Realizing the Potential of Digital Development: The Case of Agricultural Advice. Science, 366(6471), Article eaay3038. https://doi.org/10.1126/science.aay3038
Fivian, E., Parida, M., Harris-Fry, H., Mohanty, S., Padhan, S., Pradhan, R., Das, P., Odhiambo, G., Prost, A., Roopnaraine, T., Behera, S., James, P., Mishra, N. K., Rath, S., Nair, N., Rath, S., Koniz-Booher, P., Danton, H., Allen, E., and Kadiyala, S. (2024). Feasibility, Acceptability and Equity of a Mobile Intervention for Upscaling Participatory Action and Videos for Agriculture and Nutrition (m-UPAVAN) in Rural Odisha, India. PLOS Global Public Health, 4(5), Article e0003206. https://doi.org/10.1371/journal.pgph.0003206
Geethalakshmi, K., Thaloor, S., and Gajare, P. (2024). Investigating the Impact of Online Media on Agricultural Practices and Rural Development: A Content Analysis. Indian Journal of Extension Education, 60(3), 42-48. https://doi.org/10.48165/ijee.2024.60309
Glendenning, C. J., Babu, S., and Asenso-Okyere, K. (2010). Review of Agricultural Extension in India: Are Farmers' Information Needs Being Met? (IFPRI Discussion Paper No. 01048). International Food Policy Research Institute. https://hdl.handle.net/10568/154873
Godara, S., and Toshniwal, D. (2020). Sequential Pattern Mining Combined Multi-Criteria Decision-Making for Farmers' Queries Characterization. Computers and Electronics in Agriculture, 173, Article 105448. https://doi.org/10.1016/j.compag.2020.105448
Godara, S., and Toshniwal, D. (2022). Deep Learning-Based Query-Count Forecasting Using Farmers' Helpline Data. Computers and Electronics in Agriculture, 196, Article 106875. https://doi.org/10.1016/j.compag.2022.106875
Godara, S., Batra, K., Bana, R. S., Marwaha, S., and Bedi, J. (2025). AgriFact Framework for Modelling the Impact of Farmers' Information Demand on Nationwide Wheat Productivity in India. Scientific Reports, 15(1), Article 33621. https://doi.org/10.1038/s41598-025-19133-0
Godara, S., Bedi, J., Parsad, R., Singh, D., Bana, R. S., and Marwaha, S. (2024). AgriResponse: A Real-Time Agricultural Query-Response Generation System for Assisting Nationwide Farmers. IEEE Access, 12, 294-311. https://doi.org/10.1109/ACCESS.2023.3339253
Godara, S., Begam, S., Bana, R. S., Bedi, J., Jain, R., Haque, Md. A., Parsad, R., Marwaha, S., Patial, M., Shirzad, S., and Nirmal, R. (2024). TPTC: Topic-Wise Problems' Trend Clusters for Smart Agricultural Insights Extraction and Forecasting of Farmer's Information Demand. Scientific Reports, 14(1), Article 29272. https://doi.org/10.1038/s41598-024-80488-x
Godara, S., Birthal, P. S., Avinash, G., Ahmad Faiz, M., Bana, R. S., Jhajhria, A., Parsad, R., and Marwaha, S. (2024). Quantifying Effects of Climate Change and Farmers' Information Demand on Wheat Yield in India: A Deep Learning Approach With Regional Clustering. Frontiers in Sustainable Food Systems, 8, Article 1357201. https://doi.org/10.3389/fsufs.2024.1357201
Godara, S., Toshniwal, D., Bana, R. S., Singh, D., Bedi, J., Parsad, R., Dabas, J. P. S., Jhajhria, A., Godara, S., Kumar, R., and Marwaha, S. (2023). AgrIntel: Spatio-Temporal Profiling of Nationwide Plant-Protection Problems Using Helpline Data. Engineering Applications of Artificial Intelligence, 117, Article 105555. https://doi.org/10.1016/j.engappai.2022.105555
Godara, S., Toshniwal, D., Parsad, R., Bana, R. S., Singh, D., Bedi, J., Jhajhria, A., Dabas, J. P. S., and Marwaha, S. (2022). AgriMine: A Deep Learning Integrated Spatio-Temporal Analytics Framework for Diagnosing Nationwide Agricultural Issues Using Farmers' Helpline Data. Computers and Electronics in Agriculture, 201, Article 107308. https://doi.org/10.1016/j.compag.2022.107308
Guntukogula Pattabhi, S., Prashanth, P., Sreenivasulu, M., and Madavilata, A. (2023). Effectiveness of Social Media Agricultural Information on Farmer's Knowledge. Environment Conservation Journal, 24(1), 123-129. https://doi.org/10.36953/ECJ.11432297
Heeks, R. (2002). Information Systems and Developing Countries: Failure, Success, and Local Improvisations. The Information Society, 18(2), 101-112. https://doi.org/10.1080/01972240290075039
Jain, M., Kumar, P., Bhansali, I., Liao, Q. V., Truong, K., and Patel, S. (2018). FarmChat: A Conversational Agent to Answer Farmer Queries. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(4), Article 170. https://doi.org/10.1145/3287048
Jasani, D. (2022). Technology adoption barriers in construction: Behavioral, operational, and training perspectives. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 607–616.
Jasani, D. (2024). Understanding the digital skills gap in the AI forward construction workforce: A data-driven study. Applied Science and Engineering Journal for Advanced Research, 3(3), 46–51.
Jayashree, B. (2018). Social Media and Communication by Scientists: M. S. Swaminathan on Twitter. Current Science, 114(9), 1840-1845. https://doi.org/10.18520/cs/v114/i09/1840-1845
Kamilaris, A., and Prenafeta-Boldú, F. X. (2018). Deep Learning in Agriculture: A Survey. Computers and Electronics in Agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
Karthikeyan, C., Rangasami, S. S., Kumar, S. A., Ajaykumar, R., Harishankar, K., Thirunavukkarasu, M., and Karthika, R. (2024). Digital Revolution in Livestock Farming: Empowering Indian Farmers With TNAU Cattle Expert System and User Feedback Insights. Indian Journal of Animal Research, 58(9), 1622-1629. https://doi.org/10.18805/IJAR.B-5383
Kiruthiga, C., and Dharmarajan, K. (2025). Data-Driven Insights: A Critical Analysis of Farmer Call Centre Data Using Machine Learning Techniques. International Journal of Computational and Experimental Science and Engineering, 11(2), 1781-1793. https://doi.org/10.22399/ijcesen.1348
Kumar, A., and Sharma, A. (2020). Socio-Sentic Framework for Sustainable Agricultural Governance. Sustainable Computing: Informatics and Systems, 28, Article 100274. https://doi.org/10.1016/j.suscom.2018.08.006
Kumar, A., Kumar Prusty, A., Naik, A., Kumar P, N., Kumar Ojha, P., and Mounika, T. (2025). Adoption and Compliance of AI-Enabled Pest Advisories: Evidence From the National Pest Surveillance System (NPSS) in Odisha, India. Indian Journal of Extension Education, 61(4), 78-83. https://doi.org/10.48165/ijee.2025.61413
Kumar, R. (2023). Farmers' Use of the Mobile Phone for Accessing Agricultural Information in Haryana: An Analytical Study. Open Information Science, 7(1), Article 20220145. https://doi.org/10.1515/opis-2022-0145
Leeuwis, C., and Aarts, N. (2011). Rethinking Communication in Innovation Processes: Creating Space for Change in Complex Systems. The Journal of Agricultural Education and Extension, 17(1), 21-36. https://doi.org/10.1080/1389224X.2011.536344
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), Article 2674. https://doi.org/10.3390/s18082674
Mishra, G., and Shah, R. (2019). Community Building Through Social Media in a Rural Context. International Journal of Advanced Media and Communication, 7(4), Article 111167. https://doi.org/10.1504/IJAMC.2019.111167
Mittal, S., and Mehar, M. (2016). Socio-Economic Factors Affecting Adoption of Modern Information and Communication Technology by Farmers in India: Analysis Using Multivariate Probit Model. The Journal of Agricultural Education and Extension, 22(2), 199-212. https://doi.org/10.1080/1389224X.2014.997255
Mongeon, P., and Paul-Hus, A. (2016). The Journal Coverage of Web of Science and Scopus: A Comparative Analysis. Scientometrics, 106(1), 213-228. https://doi.org/10.1007/s11192-015-1765-5
Nain, M. S., Singh, R., and Mishra, J. R. (2019). Social Networking of Innovative Farmers Through WhatsApp Messenger for Learning Exchange: A Study of Content Sharing. The Indian Journal of Agricultural Sciences, 89(3), 556-558. https://doi.org/10.56093/ijas.v89i3.87605
Nakasone, E., Torero, M., and Minten, B. (2014). The Power of Information: The ICT Revolution in Agricultural Development. Annual Review of Resource Economics, 6(1), 533-550. https://doi.org/10.1146/annurev-resource-100913-012714
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Palaniswamy, V., and Raj, K. (2022). Social Media Marketing Adoption by Agriculturists: A TAM Based Study. International Journal of Professional Business Review, 7(3), Article e0537. https://doi.org/10.26668/businessreview/2022.v7i3.537
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., and Duffy, S. (2006). Guidance on the Conduct of Narrative Synthesis in Systematic Reviews: A Product From the ESRC Methods Programme. Lancaster University.
Rai, S. K., and Singh, J. P. (2025). Identification and Analysis of Indian Farmers' Behavior Toward Adoption of New Farming Technologies and E-Agriculture Schemes Through Twitter. International Journal on Smart Sensing and Intelligent Systems, 18(1), Article 20250019. https://doi.org/10.2478/ijssis-2025-0019
Rama Devi, V., and Venugopal, M. (2022). The Role of Social Media in Rural Development: A Study in Telangana State. Journal of Rural Development, 41(1), 136-152. https://doi.org/10.25175/jrd/2022/v41/i1/172467
Rehman, M. Z. U., Raghuvanshi, D., and Kumar, N. (2023). KisanQRS: A Deep Learning-Based Automated Query-Response System for Agricultural Decision-Making. Computers and Electronics in Agriculture, 213, Article 108180. https://doi.org/10.1016/j.compag.2023.108180
Ryan, M. (2023). The Social and Ethical Impacts of Artificial Intelligence in Agriculture: Mapping the Agricultural AI Literature. AI and Society, 38(6), 2473-2485. https://doi.org/10.1007/s00146-021-01377-9
Singh, M., Singh, A., Bharti, S., Singh, P., and Saini, M. (2022). Using Social Media Analytics and Machine Learning Approaches to Analyze the Behavioral Response of Agriculture Stakeholders During the COVID-19 Pandemic. Sustainability, 14(23), Article 16174. https://doi.org/10.3390/su142316174
Spielman, D. J., Lecoutere, E., Makhija, S., and Van Campenhout, B. (2021). Information and Communications Technology (ICT) and Agricultural Extension in Developing Countries. Annual Review of Resource Economics, 13(1), 177-201. https://doi.org/10.1146/annurev-resource-101520-080657
Suchiradipta, B., and Saravanan, R. (2016). Social Media: Shaping the Future of Agricultural Extension and Advisory Services (GFRAS Interest Group on ICT4RAS Discussion Paper). Global Forum for Rural Advisory Services.
Thakur, D., and Chander, M. (2017). Use of Social Media for Livestock Advisory Services: The Case of WhatsApp in Himachal Pradesh, India. The Indian Journal of Animal Sciences, 87(8), 1034-1037. https://doi.org/10.56093/ijans.v87i8.73531
Vasan, M., and Yoganandan, G. (2024). Does the Belief of Farmers on Land as God Influence the Adoption of Smart Farming Technologies? Benchmarking: An International Journal, 31(7), 2338-2359. https://doi.org/10.1108/BIJ-10-2022-0645
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