Importance of Smart Agriculture and Use of Artificial Intelligence in Shaping the Future of Agriculture

Chanda Thapliyal Nautiyal

Department of Higher Education Uttarakhand-248001, India.

Pankaj Nautiyal *

Krishi Vigyan Kendra (ICAR-CSSRI), Hardoi-II, UP- 241203, India.

Gaurav Papnai

Krishi Vigyan Kendra (ICAR-IARI), Gurugram, Haryana-122004, India.

Harsh Mittal

Department of Plant Pathology, SHUAT, Prayagraj, UP-211007, India.

Khusboo Agrawal

Texas Tech University, Lubbock, TX 79409, Texas, USA.


Department of Agronomy, DIBNS, Dehradun, Uttarakhand-248001, India.


Department of Agronomy, SGRR University, Dehradun, Uttarakhand-248001, India.

Raj Nandini

Department of ARTD, RKMVERI, Ranchi, Jharkhand-834008, India.

*Author to whom correspondence should be addressed.


India, the second-most populated country globally with 1.4 billion people, faces significant challenges in its agriculture sector, including the need to feed a growing global population, mitigate climate change impacts, and ensure sustainable resource management. To address these challenges, innovative techniques and advanced technologies are required. Modern farming techniques, coupled with artificial intelligence (A.I.), offer promising solutions. Technologies such as drones, biotechnology, genetics, and precision farming can enhance efficiency, productivity, and sustainability. Implementing these approaches can lead to a fiftyfold increase in yield and a 50% reduction in manpower, contributing to increased crop yields, reduced water usage, minimized chemical applications, and improved labor efficiency. Smart agriculture holds the potential to revolutionize the industry, significantly contributing to global food security and the preservation of natural resources.

Keywords: Precision, drones, smart agriculture, robotics, regenerative

How to Cite

Nautiyal , C. T., Nautiyal , P., Papnai , G., Mittal , H., Agrawal , K., Shivani, Vishesh, & Nandini , R. (2024). Importance of Smart Agriculture and Use of Artificial Intelligence in Shaping the Future of Agriculture. Journal of Scientific Research and Reports, 30(3), 129–138.


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Nautiyal P, Nautiyal CT, Agrawal K, Khaniya S, Semwal A, Karki B. Importance of artificial intelligence and machine learning in agriculture. International Journal of Agriculture Sciences. 2022;14 (10):11758-11760.

Dua AM. Artificial intelligence in indian agriculture. Bhajan Global Impact Foundation; 2021. Available: (retrieved on November 25, 2021).

Raj EFI, Appadurai M, Athiappan K. Precision farming in modern agriculture. In smart agriculture automation using advanced technologies: Data analytics and machine learning, cloud architecture, automation and IoT. Springer Singapore. 2022;61-87.

Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming-a review. Agric. Syst. 2017;153:69–80.

Shaikh TA, Mir WA, Rasool T, Sofi S. Machine learning for smart agriculture and precision farming: towards making the fields talk. Archives of Computational Methods in Engineering. 2022;29(7):4557-4597.

Cole JB, Newman,S, Foertter F, Aguilar I, Coffey M. Breeding and genetics symposium: really big data: processing and analysis of very large data sets. J.Anim. Sci. 2012; 90(3):723–733.

Hartmann PM, Zaki M, Feldmann N, Neely A. Capturing value from big data-a taxonomy of data-driven business models used by start-up firms. Int. J.Oper. Prod. Manag. 2016;36(10):1382–1406.

Kamilaris A, Kartakoullis A, Prenafeta-Boldú FX. A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture. 2017; 143:23-37.

Bauer J, Aschenbruck N. Design and implementation of an agricultural monitoring system for smart farming. IoT Vertical and Topical Summit on Agriculture-Tuscany (IOT Tuscany). 2018;1–6.

Farooq MS, Riaz S, Abid A, Umer T, Zikria YB. Role of IoT technology in agriculture: A systematic literature review. Electronics. 2020;9(2):319.

Sun J, Zhou Z, Bu Y, Zhuo J, Chen Y, Li D. Research and development for potted flowers automated grading system based on internet of things. J. Shenyang Agric. Univ. 2013;44(5):687–691.

Glaroudis D, Iossifides A, Chatzimisios P. Survey, comparison and research challenges of IoT application protocols for smart farming. Comput. Netw. 2020; 168:7037.

Nautiyal CT, Singh S, Rana US. Recognition of noisy numbers using neural network. In Soft Computing: Theories and Applications: Proceedings of SoCTA. Springer Singapore. 2016;2:123-132.

Thakur R, Singh BK. Importance of Artificial intelligence in agriculture. Agriblossom. 2021;1(12):23-29.

Agrawal N, Agrawal H. Artificial intelligence revolutionizing agriculture. Science Reporter. 2021;36-37.

Sreekantha DDK, Rao KPN. Applications of unmanned ariel vehicles (UAV) in agriculture: A stu dy. Int. J. Res. Appl. Sci. Eng. 2018;6:1162-1166.

Merwe DG. The Christian spirituality of the love of God: Conceptual and experiential perspectives emanating from the Gospel of John. Verbum et Ecclesia. 2020;41(1):1-10.

Yinka-Banjo C, Ajayi O. Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture. Autonomous vehicles. 2019;107-128.

Kim J, Kim S, Ju C, Son HI. Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications. IEEE Access. 2019;7:105100-105115.

Mauro DA, Greco M, Grimaldi M. A formal definition of Big Data based on its essential features. Libr. Rev. 2016;65(3):122–135.

Zhu C, Wang H, Liu X, Shu L, Yang LT, Leung VC. A Novel Sensory Data. 2014; 10(3):1125-1136.

Voulodimos AS, Patrikakis CZ, Sideridis AB, Ntafis VA, Xylouri EM. A complete farm management system based on animal identification using RFID technology. Computers and Electronics in Agriculture. 2010;70(2):380-388.

Pretty J, Guijt I, Scoones I, Thompson J. Regenerating agriculture: the Agroecology of low-external input and community-based development. In Policies for a small planet. Routledge. processing framework to integrate sensor networks with mobile cloud. IEEE Systems Journal. 2019;91-123.

Savory A, Duncan T. Regenerating agriculture to sustain civilization. In land restoration. Academic Press. 2016;289-309.

Gille KE, Hijbeek R, Andersson JA, Sumberg J. Regenerative agriculture: an agronomic perspective. Outlook on Agriculture. 2021;50(1):13-25.

Goswami S, Nautiyal P, Aswal A, Bisht R, Das S, Kamboj A, Tripathi D, Pandey S, Uniyal T, Maheshwari V. Regenerative agriculture Is new tomorrow. International Journal of Agriculture Sciences. 2021; 13(12):10998-10999.

Hawken P. Drawdown: The most comprehensive plan ever proposed to reverse global warming, New York, NY: Penguin; 2017. Available:

Moyer J, Smith A, Rui Y. Regenerative agriculture and the soil carbon solution. Kutztown PA: Rodale Institute; 2020. Available: tent/uploads/Rodale-SoilCarbonWhitePap er_v11-compressed.pdf

(accessed December 22, 2023).

Othman MM, Ishwarya KR, Ganesan M. A study on data analysis and electronic application for the growth of smart farming. Alinteri J. Agric. Sci. 2021;36(1):209–218.

Rani MU, Kamalesh S. Energy efficient fault tolerant topology scheme for precision

agriculture using wireless sensor network. IEEE. 2014;1208-1211.

Kim Y, Evans, R.G. and Iversen, W.M. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE transactions on instrumentation and measurement. 2008; 57(7):1379-1387.

Kim Y, Evans R. Software design for wireless sensor-based site-specific irrigation. Computers and Electronics in Agriculture. 2009;66(2):159-165.

Gang LL. Design of greenhouse environment monitoring and controlling system based on bluetooth technology. Transactions of the Chinese Society for Agricultural Machinery. 2006;10:97-100.

Mohapatra AG, Lenka SK. Neural network pattern classification and weather dependent fuzzy logic model for irrigation control in WSN based precision agriculture. Procedia Computer Science. 2016;78:499-506.

Mendez GR, Mukhopadhyay SC. Wireless sensor networks and ecological monitoring. 2013:247-268.

Gutiérrez J, Villa-Medina JF, Nieto-Garibay A, Porta-Gándara MÁ. Automated irrigation system using a wireless sensor network and GPRS module. IEEE Transactions on Instrumentation and Measurement. 2013;63(1):166-176.

Gil-Lebrero S, Quiles-Latorre FJ, Ortiz-López M, Sánchez-Ruiz V, Gámiz-López V, LunaRodríguez, JJ. Honey bee colonies remote monitoring system. Sensors. 2016;17(1):55.

Ilie-Ablachim D, Pătru GC, Florea IM, Rosne D. Monitoring device for culture substrate growth parameters for precision agriculture: Acronym: MoniSen. 2016;1-7.

Llaria A, Terrasson G, Arregui H, Hacala A. Geolocation and monitoring platform for extensive farming in mountain pastures. 2015;2420-2425.

Terrasson G, Llaria A, Marra A, Voaden, S. Accelerometer based solution for precision livestock farming: Geolocation enhancement and animal activity identification, IOP Publishing, 2016; 012004.

Carolan M. Acting like an algorithm: Digital farming platforms and the trajectories they (need not) lock-in. In: Desa G, Jia X. (eds.) Social innovation and sustainability transition. Springer Nature Switzerland. 2020;107–119.

Ideaforge. AI-Powered Drones for Precision Agriculture: The Secret to Scaling and Sustenance; 2020.


(accessed November 11, 2023).

Nautiyal CT, Rana US, Kumar R. classification of noisy English alphabets using neural network. In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT). IEEE. 2016;704-709.

Ruiz-Garcia L, Lunadei L. The role of RFID in agriculture: Applications, limitations and challenges. Computers and Electronics in Agriculture. 2011;79(1):42-50.