How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis
DOI:
https://doi.org/10.18034/apjee.v6i2.542Keywords:
Environmental sustainability, Artificial intelligence, Agricultural productivity, Thematic analysisAbstract
In the face of the agricultural sector's challenges, food security with an increasing human population and high demand for food is a significant problem. Traditional methods used by farmers have not been sufficient to meet the food requirements of the growing population. As a result, the agricultural sector has begun to deploy artificial intelligence to meet the demand for food and sustainability. This study was conducted to examine how AI improves farmers' productivity and sustainability. Data were analyzed using centering resonance analysis, t-test, ANOVA, and text mining news articles from 2014-2019 in Africa, Asia, Europe, and North America. Results show that AI is used primarily to increase productivity and efficiency and secondarily to address labor shortages and environmental sustainability concerns. The results at the regional level reflect the active adoption of AI in North America and Europe, with increasing efforts in Asia and Africa.
Downloads
References
Acemoglu, D. and Restrepo, P. (2018). Artificial Intelligence, Automation, and Work. NBER Working Paper No. 24196 (National Bereau of Economic Research, 2018).
Akinola, A. A., A. D. Alene, R. Adeyemo, D. Sanogo, A. S. Olanrewaju, C. Nwoke, G. Nziguheba, and J. Diels. (2010). “Determinants of Adoption and Intensity of Use of Balanced Nutrient Management Systems Technology in the Northern Guinea Savanna of Nigeria.”. Quartely Journal of International Agriculture , 49(1):25-45.
Andersen MA, Alston JM, Pardey PG, Smith A. (2018). A century of U.S. productivity growth: a surge then a slowdown. Am J Agric Econ , 93:1257-1277.
B. Recio, F. Rubio, and J.A. Criado, (2003). “A decision support system for farm planning using AgriSupport II.” Decision Support Systems, vol. 36, no.2,, pp.189-203.
B.A. Aubert, A. Schroeder, and J. Grimaudo, (2012). “IT as enabler of sustainable farming: an empirical analysis of farmers adoption decision of precision agriculture technology,” Decision Support System, vol. 54, pp. 510-512.
Bollier, D. (2017). Artificial intelligence comes of age. The promise and challenge of integrating AI into cars, healthcare and journalism. The Aspen Institute Communications and Society Program.Washington, DC.
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V. & Kalai, A. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Adv. Neural Inf. Process. Syst. , 29, 4349–4357.
C.L. Rossetti, and K.J. Dooley,. (2010). Job types in the supply chain management profession. Journal of Supply Chain Management, vol.46, 2010, pp. 40–56.
Canary, H. E., and Jennings, M. M. (2008). Principal and influence in codes of ethics: A centering resonance analysis comparing pre-and post – Sarbanes-Oxley Codes of ethics. Journal of Business Ethics, , 80(2), 263–278.
Cavallo, E., E. Ferrari, L. Bollani, and M. Coccia,. (2014). Attitudes and Behaviour of Adopters of Technological Innovations in Agricultural Tractors: A Case Study in Italian Agricultural System. Agricultural Systems, , vol.130, pp.44–54.
Chan, C. W. and Huangb, G. H., (2003). Artificial intelligence for management and control of pollution minimisation and mitigation processes. Engineering Applications of Artificial Intelligence, Vol. 16 Issue 2, pp.75-90,.
Chen, X. and Van Beek, P., (2001). Conflict-directed backjumping revisited. Journal of Artificial Intelligence Research, Vol. 14, pp.53-81,.
Chui, M. and Francisco, S.,. (2017). Artificial intelligence the next digital frontier?. Company Global Institute,.
Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P. and Malhotra, S.,. (2018). NOTES FROM THE AI FRONTIER INSIGHTS FROM HUNDRED OF USE CASES.pp.2-7.
Coble KH, Mishra AK, Ferrell S, Griffin T. (2018). Big data in agriculture: a challenge for the future. . Appl Econ Perspect Policy , 40:79-96.
Corman, S., Kuhn, T., McPhee, R., & Dooley, K. (2002). Studying complex discursive systems: Centering resonance analysis of communication. . Human Communication Research, 28, 157-206.
Donepudi, P. K. (2014a). Technology Growth in Shipping Industry: An Overview. American Journal of Trade and Policy, 1(3), 137-142. https://doi.org/10.18034/ajtp.v1i3.503
Donepudi, P. K. (2014b). Voice Search Technology: An Overview. Engineering International, 2(2), 91-102. https://doi.org/10.18034/ei.v2i2.502
Donepudi, P. K. (2017). AI and Machine Learning in Banking: A Systematic Literature Review. Asian Journal of Applied Science and Engineering, 6(3), 157–162. http://doi.org/10.5281/zenodo.4109672
Donepudi, P. K. (2018). AI and Machine Learning in Retail Pharmacy: Systematic Review of Related Literature. ABC Journal of Advanced Research, 7(2), 109-112. https://doi.org/10.18034/abcjar.v7i2.514
Duckett T, Pearson S, Blackmore S, Grieve B, Smith M . (2018). White paper – agricultural robotics: the future of robotic agriculture. In ‘EPRSC, 2018 international robotics showcase’. . Retrieved from Available at http://eprints.uwe.ac.uk/36839
E. Gelb, A. Maru, J. Brodgen, E. Dodsworth, R. Samii, V. Pesce. (2008). Adoption of ICT* Enabled Information Systems for Agricultural Development and Rural Viability, Atsugi Japan, p. 4-5.
E. Rich and Kevin Knight. (1991). "Artificial intelligence", . New Delhi: McGraw-Hill, .
Freitas, J. S., Ferreira, J. C. A., Campos, A. A. R., de Melo, J. C. F., Cheng, L. C., and Gonçalves, C. A. . (2018). Methodological roadmapping: A study of Centering Resonance Analysis. RAUSP Management Journal,, 53(3),459–475. https://doi.org/10.1108/RAUSP-04-2018-005
G. Fox, J. Mooney, and P. Rosati,. (2018). “Towards an understanding of farmers mobile technology adoption: a comparison of adoption and continuance intentions,” AMCIS, New Orleans, .
Galon, N. (2010). The use of pedometry for estrus detection in dairy cows in Israel. The Journal of Reproduction and Development, 56, S48–S52. https://doi.org/10.1262/jrd.1056S48
Grosz, B., Weinstein, S., and Joshi, A. (1995). Centering: A framework for modeling the local coherence of a discourse. Computational Linguistics, 21,203-225.
Grosz, B.J., Altman, R., Horvitz, E., Mackworth, A., Mitchell, T., Mulligan, D. and Shoham, Y.,. (2016). Artificial Intelligence and life in 2030: One hundred year study on artificial intelligence. Stanford University.
H. Auernhammer,. (2001). “Precision farming - The environmental challenge,” Computers and Electronics in Agriculture, vol.30, 2001, pp. 31–43.
H. Kagaya, K. Aizawa, M. Ogawa, . (2014). Food detection and recognition using convolutional neural network, in: Proceedings of the 22nd ACM International Conference on Multimedia, 2014, pp. 1085–1088.
Hatfield J, Takle G, Grotjahn R, Holden P, Izaurralde RC, Mader T, Marshall E, Liverman D: Ch. 6. (2014). : Agriculture. In Climate change in the United States: The Third National Climate Assessment. Edited by Melillo JM, Richmond T, Yohe GW. U.S. Global Change Research Program:50-174.
Hong, J. (2001). Goal recognition through goal graph analysis, Journal of Artificial Intelligence Research,Vol. 15, pp.1-30,.
International Energy Agency (2017). International Energy Agency. Digitalization & Energy .
Kakkad, V., Patel, M., Shah, M.,. (2019). Biometric authentication and image encryption for image security in cloud framework. Multiscale and Multidiscip. Model. Exp. and Des., 1–16. Retrieved from https://doi.org/10.1007/s41939-019-00049-y
Kannagi L, Ramya C, Shreya R, Sowmiya R . (2018). Virtual conversational assistant: ‘The FARMBOT’. International Journal of Engineering Technology Science and Research , 5, 520–527.
Kaplan, A.; Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 2019, 62, 15–25.
Kassie M, Shiferaw B, Muricho G . (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. . World Dev , 39(10):1784–1795.
Kim, Y.J., Evans, R.G., Iversen, W.M.,. (2008). Remote sensing and control of an irrigation system using a distributed wireless sensor network. . IEEE Trans. Instrum. Meas., (pp. 57 (7), 1379–1387).
Kisauzi, T., Mangheni, M. N., Sseguya, H. and Bashaasha, B. (2012). Gender dimensions of farmers’ perceptions and knowledge on climate change in Teso Sub - Region, Eastern Uganda. African Crop Science Journal, 20(2): 275 – 286.
Ladebo, O. (2004). Job Behaviour and Attitude of Agricultural Faculty: Beyond the influence of Biographical factors. . Journal of extension system , 20 (2)), pp. 89 – 103.
Liakos, K., Busato, P., Moshou, D., Pearson, S., Bochtis, D.,. (2018). Machine Learning in Agriculture: A Review. Sensors 18 (8), 2674. Retrieved from https://doi.org/10.3390/s18082674
Maertens A, Barrett CB. (2013). Measuring Social Networks' Effect on Agricultural Technology Adoption. . Am J Agric Econ , 95(2):353–359.
Marr, B. (2018). What is artificial intelligence and how will it change our world? Retrieved from https://www.bernardmarr.com/default.asp?contentID=963
McLaren, T., Vuong, D., & Grant, K. (2007). Do you know what you don't know? Critical reflection and concept mapping in an Information system strategy course. communications of the Association for Information Systems. 20,892-908.
McPhee, R. D., Corman, S. R., and Dooley, K. (2002). Organizational knowledge expression and management. . Management Communication Quarterly,, 16(2), 274–281.
Mikalef, P.; Framnes, V.A.; Danielsen, F.; Krogstie, J.; Olsen, D. (2017, 16-20 July). Big Data Analytics Capability: Antecedents and Business Value. In Proceedings of the Pacific Asia Conference on Information Systems, Langkawi Island, Malaysia; p. 136.
N.R. Kitchen, C.J. Snyder, D.W. Franzen, W.J. Wiebold. (2002). “Educational needs of precision agriculture,”. Precision Agriculture,, vol.3, pp.341–351.
National Artificial Intelligence Research and Development Strategic Plan. (2016, october). Executive office of the president of the United States. National Science and technology Council and Networking and information technology Research and development subcommitee. Retrieved from https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf
Nilsson, N.J. (1983). Artificial intelligence prepares for 2001. AI Magazine, 15 December 1983; p. 7.
Norouzzadeh, M. S. et al. . (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. . Proc. Natl Acad. Sci. USA 115, E5716–E5725.
Norvig, P. (2002.). , S.A. Intelligence, A Modern Approach, Prentice Hall,.
Oppenheim, D. and Shani, G. (2017). Potato Disease Classification Using Convolution Neural Networks. ECPA. 244–249.
Panpatte, D.G.,. (2018). Artificial Intelligence in Agriculture: An Emerging Era of Research. Intutional Science, CANADA, pp. 1–8.
Peng Y. and Zhang X. (2007). Integrative data mining in systems biology: from text to network mining,. Artificial Intelligence in Medicine,, Vol. 41, No. 2, pp.83-86,.
Purdy, M., and Daugherty, P. 2016. (n.d.). "Why Artificial Intelligence Is the Future of Growth," Remarks at AI Now: The Social and Economic Implications of Artificial Intelligence Technologies in the Near Team). pp.1-72.
Ransbotham, S., David Kiron, Philipp Gerbert and M. Reeves. (2017). Reshaping Business With Artificial Intelligence. MIT Sloan Management Review.
Ransbotham, S., Gerbert, P., Reeves, M.A.R.T.I.N., Kiron, D.A.V.I.D. and Spira, M.I.C.H.A.E.L.,. (2018). Artificial Intelligence in Business Gets Real. MIT Sloan Management Review .
Ribeiro, A.–Fernandez-Quintanill, C.–Dorado, J.–Lopez-Granados, F.–Pena, J. M.–Rabatel, G.–Perez-Ruiz, M.–Conesa-Mufi, J.–Gonzalez deSantos, P. (2015). A fleet of aerial and ground robots: a scalable approach for autonomous site-specific herbicide application. ECPA . 167-173.
Rossetti, C. L., Handfield, R., and Dooley, K. J. (2011). Forces, trends, and decisions in pharmaceutical supply chain management. International Journal of Physical Distribution & Logistics Management., 41(6), 601–622. https://doi.org/10.1108/09600031111147835
Rutten CJ, Velthuis AGJ, Steeneveld W, Hogeveen H. (2013). Invited review: sensors to support health management on dairy farms. Journal of Dairy Science, 96, 1928–1952. https://doi.org/10.3168/jds.2012-6107
S. Corman, and K. Dooley. (2006). “Crawdad text analysis system 2.0”. Chandler, AZ: Crawdad Technologies, LLC,.
S. Fountas, S. Blackmore, D. Ess, S. Hawkins, G. Blumhoff, J. Lowenberg-Deboer, et al. (2005). “Farmer Experience with Precision Agriculture in Denmark and the US Eastern Corn Belt,”. Precision Agriculture, , vol. 6, pp. 121–41.
S.A. Augustin-Behravesh, and K. Dooley. (2018). “Differentiating Sustainably": Relating organizational culture to corporate sustainability strategies,” AOM, Chicago.
S.G. Daberkow, and W.D. McBride,. (2003). “Farm and Operator Characteristics Affecting the Awareness and Adoption of Precision Agriculture Technologies in the US,” . Precision Agriculture, , vol.4, no.2,, pp.163-177.
Sachs, J.D.; Schmidt-Traub, G.; Mazzucato, M.; Messner, D.; Nakicenovic, N.; Rockström, J. (2019). Six Transformations to achieve the Sustainable Development Goals. Nat. . Sustain. , 2,805–814.
Shideed KH, Mohammed E . (2005). Adoption and Impact Assessment of Improved Technologies in Crop and Livestock Production Systems in the WANA Region. The Development of Integrated Crop/Livestock Production in Low Rainfall Areas of Mashreq and Maghreb Regions(Mashreq/Maghreb Project).
Singer, J., Gent, I. P. and Smaill, A., . (2000). Backbone fragility and the local search cost peak, . Journal of Artificial Intelligence Research, , Vol. 12, pp.235-270.
Sirosh, J. (2018). Planet-scale land cover classification with FPGAs. In ‘Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining’, 19–23 August. pp. 2877–2877. (ACM: London).
Sjaak Wolfert, Lan Ge, Cor Verdouw, and Marc-Jeroen Bogaardt, (2017). “Big data in smart farming–a review, ” Agricultural Systems, , 153, 69 .
Stone, P., Littman, M.L., Singh, S., Kearns, M., ATTAC-. (2001). An adaptive autonomous bidding agent, . Journal of Artificial Intelligence Research, , Vol. 15, pp. 189-206.
Tate, L.M. Ellram., and J.F. N Kirchoff, . (2010). W.L.“Corporate social responsibility reports: a thematic analysis related to supply chain management,” . Journal of Supply Chain Management, vol. 46, pp. 19-44.
Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence (Random House Audio Publishing Group,).
U. Cortés & M. Sànchez-Marrè (Eds.). (2000). Workshop Notes on 2nd ECAI Workshop on Binding Environmental Sciences and Artificial Intelligence, European Conference on Artificial Intelligence Berlin.
Ullah, A., Ahmad, J., Muhammad, K., Lee, M.Y.,. (2017). A Survey on Precision Agriculture: Technologies and Challenges. The 3rd International Conference on Next Generation Computing(ICNGC2017b.
Vadlamudi, S. (2016). What Impact does Internet of Things have on Project Management in Project based Firms?. Asian Business Review, 6(3), 179-186. https://doi.org/10.18034/abr.v6i3.520
Vadlamudi, S. (2017). Stock Market Prediction using Machine Learning: A Systematic Literature Review. American Journal of Trade and Policy, 4(3), 123-128. https://doi.org/10.18034/ajtp.v4i3.521
Vadlamudi, S. (2018). Agri-Food System and Artificial Intelligence: Reconsidering Imperishability. Asian Journal of Applied Science and Engineering, 7(1), 33-42. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1192
Vasisht D, Kapetanovic Z, Won J, Jin X, Chandra R, Sinha SN, Kapoor A, Sudarshan M, Stratman S. (2017). FarmBeats: an IoT platform for datadriven agriculture. In ‘Proceedings of the 14th USENIX Symposium on Networked Systems (NSDI ’17)’, 27–29 March 2017, Boston, MA.pp.515-529.
Wang S., Wang Y., Du W., Sun F., Wang X., Zhou C. and Liang Y., . (2007). A multi-approaches-guided genetic algorithm with application to operon prediction, Artificial Intelligence in Medicine, Vol. 41,No. 2, pp.151-159, 2007.
Y. Wang, L. Jin, and H. Mao, . (2019). “Farmer cooperatives intention to adopt agricultural information technology- mediating effects of attitude,. ” Information Systems Frontiers, , pp.1-16.
Yang, H., Liusheng, W., Junmin, X. Hongli, Jan. (2007). Wireless Sensor Networks for Intensive Irrigated Agriculture, . Consumer Communications and Networking Conference, 2007. CCNC 2007. 4th IEEE. pp. 197–201 Las Vegas, Nevada.
Yunhe, P. (2016). Heading toward artificial intelligence. . Engineering Journal, , 2, pp.409-413.
Zhou X., Liu B., Wu Z. and Feng Y. (2007). Integrative mining of traditional Chines medicine literature and MEDLINE for functional gene networks. Artificial Intelligence in Medicine, Vol. 41, No. 2,pp.87-104.
--0--
Downloads
Published
Issue
Section
License
How to Cite
