Abstract
It is widely acknowledged that adoption of sustainable policies is a tool for the inclusive growth of the country. Although, manufacturing industry has received a considerable attention as compared to service industry, sustainability in agriculture is the newest approach. The purpose of this study is to explore key indicators of sustainable smart agriculture driven by big data. First, we identify the key indicators that affect sustainability and then investigate the contextual relationships among them. Modified total interpretive structural (m-TISM) is employed to investigate the interrelationships amongst the identified indicators. The findings indicate that the weather prediction and big data learning are the key indicators with high driving power. It implies that any change in these indicators would bring a significant change in other key indicators as well. Therefore, managers are cautioned to continuously monitor and deal with them with utmost care.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
FAO (2017) E-agriculture in action. Rome, Italy,
Kamilaris A, Kartakoullis A, Prenafeta-Boldú FX (2017) A review on the practice of big data analysis in agriculture. Comput Electron Agric 143:23–37
Singh NP (2016) Application of data warehouse and big data technology in agriculture in India, pp 1–8
Huang Y, Chen ZX, Tao YU, Huang XZ, Gu XF (2018) Agricultural remote sensing big data: Management and applications. J Integr Agric 17(9):1915–1931
Bendre MR, Thool RC, Thool VR (2016) Big data in precision agriculture: Weather forecasting for future farming. In: International conference on next generation computing technologies, NGCT 2015, pp 744–750
Channe H, Kothari S, Kadam D, Professors A (2015) Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud—computing, mobile—computing and big—data analysis keywords. Int J Comput Technol Appl 6:374–382
Belaud JP, Prioux N, Vialle C, Sablayrolles C (2019) Big data for agri-food 4.0: application to sustainability management for by-products supply chain. Comput Ind 111:41–50
Sushil S (2012) Interpreting the interpretive structural model. Glob J Flex Syst Manage 13(2):87–106
Ajmera P, Jain V (2019) Modeling the factors affecting the quality of life in diabetic patients in India using total interpretive structural modelling. Benchmarking Int J 26(3):951–970
Sindhwani R, Malhotra V (2017) A framework to enhance agile manufacturing system. Benchmarking Int J 24(2):467–487
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dogra, N., Adil, M. (2021). Identifying Indicators of Sustainable Smart Agriculture Driven by Big Data Using Modified Total Interpretive Structural Modeling (mTISM). In: Singh, J., Kumar, S., Choudhury, U. (eds) Innovations in Cyber Physical Systems. Lecture Notes in Electrical Engineering, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-16-4149-7_45
Download citation
DOI: https://doi.org/10.1007/978-981-16-4149-7_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4148-0
Online ISBN: 978-981-16-4149-7
eBook Packages: Computer ScienceComputer Science (R0)