Abstract
Artificial Intelligence (AI) plays a vital role in the agriculture sector. Its use in the agriculture industry to improve farming practices has increased over time. The uniqueness of AI in agriculture is its potential to transform conventional agricultural practices, opening the doors to greater productivity, sustainability, and, ultimately, a more secure global food supply. However, there are obstacles that limit the application of AI in this industry. Through a well-organized literature review, the study identified nine barriers that hinder the implementation of AI. To finalize the barriers for further investigation, the Delphi approach was employed. The barriers were analysed through modified total interpretive structural modelling (m-TISM) technique and categorized into 4 clusters using the Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis. Lack of skilled workforce and extreme climatic conditions are major driving barriers that prevent effective AI adoption. Based on the findings, the study puts forward three propositions. Timely action on the recommendation can help mitigate the concerns and benefit the stakeholders in the agriculture sector.
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Abdelmeguid A, Afy-Shararah M, Salonitis K (2022) Investigating the challenges of applying the principles of the circular economy in the fashion industry: a systematic review. Sustain Prod Consumpt 32:505–518. https://doi.org/10.1016/j.spc.2022.05.009
Agrawal A (2020) Modified total interpretive structural model of corporate financial flexibility. Glob J Flex Syst Manag 21(4):369–388. https://doi.org/10.1007/s40171-020-00253-7
Alzoubi I, Delavar M, Mirzaei F, Nadjar Arrabi B (2017) Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling. Int J Energy Sect Manage 11(4):522–540. https://doi.org/10.1108/IJESM-01-2017-0003
Balakrishna K, Mohammed F, Ullas CR, Hema CM, Sonakshi SK (2021) Application of IOT and machine learning in crop protection against animal intrusion. Global Transit Proceed 2(2):169–174. https://doi.org/10.1016/J.GLTP.2021.08.061
Behl A, Rathi P, Ajith Kumar VV (2018) Sustainability of the Indian auto rickshaw sector: Identification of enablers and their interrelationship using TISM. Int J Serv Operat Manag 31(2):137–168. https://doi.org/10.1504/IJSOM.2018.094750
Behl A, Pereira V, Sindhwani R, Bhardwaj S, Papa A, Hassan Y (2022) Improving inclusivity of digitalization for employees in emerging countries using gamification. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3216553
Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: a comprehensive updated review. Sensors 21(11):3758
Bhargava A, Bester M, Bolton L (2021) Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. J Technol Behav Sci 6(1):106–113. https://doi.org/10.1007/s41347-020-00153-8
Bhat SA, Huang NF (2021) Big data and ai revolution in precision agriculture: survey and challenges. IEEE Access 9:110209–110222. https://doi.org/10.1109/ACCESS.2021.3102227
Blessy JA, Kumar A (2021) Smart irrigation system techniques using artificial intelligence and IoT. In: Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 1355–1359. https://doi.org/10.1109/ICICV50876.2021.9388444.
Bouguettaya A, Zarzour H, Kechida A, Taberkit AM (2022) Deep learning techniques to classify agricultural crops through UAV imagery: a review. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07104-9
Chatterjee S (2020) AI strategy of India: policy framework, adoption challenges and actions for government. Transform Gover People Process Policy 14(5):757–775. https://doi.org/10.1108/TG-05-2019-0031
Crane-Droesch, A. (2018). Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environ Res Lett. https://doi.org/10.1088/1748-9326/aae159
da Silveira F, da Silva SLC, Machado FM, Barbedo JGA, Amaral FG (2023) Farmers’ perception of barriers that difficult the implementation of agriculture 4.0. Agric Syst. https://doi.org/10.1016/j.agsy.2023.103656
Delgado J, Short NM, Roberts DP, Vandenberg B (2019) Big data analysis for sustainable agriculture. Front Sustain Food Syst 3:54
Dhanabalan T, Sathish A (2018) Transforming Indian industries through artificial intelligence and robotics in industry 4.0. Int J Mech Eng Technol 9(10):835–845
Dharmaraj V, Vijayanand C (2018) 18–2 Tarımda Yapay Zeka (AI). Int J Curr Microbiol Appl Sci 7(12):2122–2128
Gao F, Shen Y, Sallach JB, Li H, Zhang W, Li Y, Liu C (2022) Predicting crop root concentration factors of organic contaminants with machine learning models. J Hazard Mater 424:127437. https://doi.org/10.1016/J.JHAZMAT.2021.127437
Genze N, Bharti R, Grieb M, Schultheiss SJ, Grimm DG (2020) Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops. Plant Methods. https://doi.org/10.1186/s13007-020-00699-x
Ghosh I, Banerjee G, Sarkar U, Bannerjee G, Das S (2018) Artificial Intelligence in Agriculture: A Literature Survey Artificial Intelligence in Agriculture: A Literature Survey View project Site Specific Crop Recommendation View project Artificial Intelligence in Agriculture: A Literature Survey. In: International Journal of Scientific Research in Computer Science Applications and Management Studies IJSRCSAMS (Vol. 7, Issue 3). www.ijsrcsams.com
Guillén MA, Llanes A, Imbernón B, Martínez-España R, Bueno-Crespo A, Cano JC, Cecilia JM (2021a) Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. J Supercomput 77(1):818–840. https://doi.org/10.1007/s11227-020-03288-w
Guillén MA, Llanes A, Imbernón B, Martínez-España R, Bueno-Crespo A, Cano JC, Cecilia JM (2021b) Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. J Supercomput. https://doi.org/10.1007/s11227-020-03288-w
Gupta R, Kumar V, Kaushik AK, Gupta DD, Sindhwani R (2023) Investigating the impact of online brand communities on online customer engagement and brand loyalty. J Global Market. https://doi.org/10.1080/08911762.2023.2180789
Javaid M, Haleem A, Khan IH, Suman R (2023) Understanding the potential applications of artificial intelligence in agriculture sector. Adv Agrochem 2(1):15–30. https://doi.org/10.1016/j.aac.2022.10.001
Kankanhalli A, Charalabidis Y, Mellouli S (2019) IoT and AI for smart government: a research agenda. Gov Inf Q 36(2):304–309. https://doi.org/10.1016/j.giq.2019.02.003
Karanth S, Benefo EO, Patra D, Pradhan AK (2022) Importance of artificial intelligence in evaluating climate change and food safety risk. J Agric Food Res. https://doi.org/10.1016/j.jafr.2022.100485
Katiyar S, Farhana A (2021) Smart agriculture: the future of agriculture using AI and IoT. J Comput Sci 17(10):984–999. https://doi.org/10.3844/jcssp.2021.984.999
Kshetri N (2020) Artificial intelligence in developing countries. IT Prof 22(4):63–68
Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, Bansal RC (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev 69:596–609
Kumar R, Sindhwani R, Arora R, Singh PL (2021) Developing the structural model for barriers associated with CSR using ISM to help create brand image in the manufacturing industry. Int J Adv Operat Manag 13(3):312–330
Kumar V, Sindhwani R, Behl A, Kaur A, Pereira V (2023) Modelling and analysing the enablers of digital resilience for small and medium enterprises. J Enterp Inf Manag. https://doi.org/10.1108/JEIM-01-2023-0002
Kumar K, Dhillon VS, Singh PL, Sindhwani R (2019) Modeling and analysis for barriers in healthcare services by ISM and MICMAC analysis. In: Advances in interdisciplinary engineering: select proceedings of FLAME 2018 (pp 501–510). Springer, Singapore
Li Q, Li Z, Shangguan W, Wang X, Li L, Yu F (2022) Improving soil moisture prediction using a novel encoder-decoder model with residual learning. Comput Electron Agric 195:106816. https://doi.org/10.1016/j.compag.2022.106816
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18(8):2674
Liu Y, Ma X, Shu L, Hancke GP, Abu-Mahfouz AM (2021) From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans Indus Inf 17(6):4322–4334. https://doi.org/10.1109/TII.2020.3003910
Malhotra C, Anand R (2020) Accelerating public service delivery in India: application of Internet of Things and artificial intelligence in agriculture. In: ACM international conference proceeding series, 62–69. https://doi.org/10.1145/3428502.3428510
Mann ML, Warner JM, Malik AS (2019) Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia. Clim Change 154(1–2):211–227. https://doi.org/10.1007/s10584-019-02432-7
Meena A, Dhir S, Sushil (2021) An analysis of growth-accelerating factors for the Indian automotive industry using modified TISM. Int J Product Perform Manag 70(6):1361–1392. https://doi.org/10.1108/IJPPM-01-2019-0047
Mehr H (2017) Artificial intelligence for citizen services and government. In: Harvard Ash Center Technology and Democracy, pp 1–16. https://ash.harvard.edu/files/ash/files/artificial_intelligence_for_citizen_services.pdf.
Mekonnen Y, Namuduri S, Burton L, Sarwat A, Bhansali S (2020) Review—machine learning techniques in wireless sensor network based precision agriculture. J Electrochem Soc 167(3):37522. https://doi.org/10.1149/2.0222003jes
Mittal VK, Sindhwani R, Kalsariya V, Salroo F, Sangwan KS, Singh PL (2017) Adoption of integrated lean-green-agile strategies for modern manufacturing systems. Procedia Cirp 61:463–468
Mittal VK, Sindhwani R, Shekhar H, Singh PL (2019) Fuzzy AHP model for challenges to thermal power plant establishment in India. Int J Operat Res 34(4):562–581
Mittal VK, Sindhwani R, Lata Singh P, Kalsariya V, Salroo F (2018) Evaluating significance of green manufacturing enablers using MOORA method for Indian manufacturing sector. In: Proceedings of the international conference on modern research in aerospace engineering: MRAE-2016 (pp. 303–314). Springer, Singapore
Mores A, Borrelli GM, Laidò G, Petruzzino G, Pecchioni N, Amoroso LGM, Marone D (2021) Genomic approaches to identify molecular bases of crop resistance to diseases and to develop future breeding strategies. Int J Mol Sci 22(11):5423
Nandakumar SD, Valarmathi R, Juliet PS, Brindha G (2021) Artificial neural network for rainfall analysis using deep learning techniques. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1964/4/042022
Oikonomidis A, Catal C, Kassahun A (2022) Deep learning for crop yield prediction: a systematic literature review. N Z J Crop Hortic Sci 51(1):1–26
Orchi H, Sadik M, Khaldoun M (2022) On using artificial intelligence and the Internet of Things for crop disease detection: a contemporary survey. Agriculture 12(1):9
Ouafiq EM, Saadane R, Chehri A (2022) Data management and integration of low power consumption embedded devices IoT for transforming smart agriculture into actionable knowledge. Agriculture 12(3):329
Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, Mcdonald S, Mckenzie JE et al (2021) PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. https://doi.org/10.1136/bmj.n160
Paudel D, Boogaard H, de Wit A, van der Velde M, Claverie M, Nisini L, Janssen S, Osinga S, Athanasiadis IN (2022) Machine learning for regional crop yield forecasting in Europe. Field Crop Res 276:108377. https://doi.org/10.1016/J.FCR.2021.108377
Rajan R, Rana NP, Parameswar N, Dhir S, Sushil, Dwivedi YK (2021) Developing a modified total interpretive structural model (M-TISM) for organizational strategic cybersecurity management. Technol Forecast Soc Change 170(January):120872. https://doi.org/10.1016/j.techfore.2021.120872
Ruben RB, Varthanan PA (2019) Application of total interpretive structural modeling application of total interpretive structural modeling application of total interpretive structural modeling (TISM) approach for analysis of barriers in deploying circular supply chains. Indian J Sci Technol 12(24):1–6. https://doi.org/10.17485/ijst/2019/v12i24/144818
Ryan M (2022) The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature. AI Soc. https://doi.org/10.1007/s00146-021-01377-9
Said Mohamed E, Belal AA, Kotb Abd-Elmabod S, El-Shirbeny MA, Gad A, Zahran MB (2021) Smart farming for improving agricultural management. Egyptian J Remote Sens Space Sci 24(3):971–981. https://doi.org/10.1016/j.ejrs.2021.08.007
Sharma M, Luthra S, Joshi S, Kumar A (2022) Implementing challenges of artificial intelligence: evidence from public manufacturing sector of an emerging economy. Gov Inf Q 39(4):101624
Sindhwani R, Mittal VK, Singh PL, Aggarwal A, Gautam N (2019) Modelling and analysis of barriers affecting the implementation of lean green agile manufacturing system (LGAMS). Benchmark Int J 26(2):498–529
Sindhwani R, Behl A, Sharma A, Gaur J (2022a) What makes micro, small, and medium enterprises not adopt Logistics 4.0? A systematic and structured approach using modified-total interpretive structural modelling. Int J Logist Res Appl. https://doi.org/10.1080/13675567.2022.2081672
Sindhwani R, Hasteer N, Behl A, Varshney A, Sharma A (2022b) Exploring “what”, “why” and “how” of resilience in MSME sector: a m-TISM approach. Benchmarking. https://doi.org/10.1108/BIJ-11-2021-0682
Sood K, Singh S, Behl A, Sindhwani R, Kaur S, Pereira V (2023) Identification and prioritization of the risks in the mass adoption of artificial intelligence-driven stable coins: the quest for optimal resource utilization. Resour Policy 81:103235
Sott MK, Furstenau LB, Kipper LM, Giraldo FD, Lopez-Robles JR, Cobo MJ, Zahid A, Abbasi QH, Imran MA (2020) Precision techniques and agriculture 4.0 technologies to promote sustainability in the coffee sector: state of the art, challenges and future trends. IEEE Access 8:149854–149867. https://doi.org/10.1109/ACCESS.2020.3016325
Spanaki K, Karafili E, Sivarajah U, Despoudi S, Irani Z (2021) Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations. Product Plann Control. https://doi.org/10.1080/09537287.2021.1882688
Su J, Sayyad-Shirabad J, Matwin S (2011) Large scale text classification using semi-supervised multinomial naive bayes. In: Proceedings of the 28th international conference on machine learning, ICML 2011, 97–104.
Thomas RL, Uminsky D (2022) Reliance on metrics is a fundamental challenge for AI. Patterns 3(5):100476
Trivelli L, Apicella A, Chiarello F, Rana R, Fantoni G, Tarabella A (2019) From precision agriculture to industry 4.0: unveiling technological connections in the agrifood sector. British Food J 121(8):1730–1743. https://doi.org/10.1108/BFJ-11-2018-0747
Vaishnavi V, Suresh M, Dutta P (2019) A study on the influence of factors associated with organizational readiness for change in healthcare organizations using TISM. Benchmark Int J 26(4):1290–1313
Valle-Cruz D, Criado JI, Sandoval-Almazán R, Ruvalcaba-Gomez EA (2020) Assessing the public policy-cycle framework in the age of artificial intelligence: from agenda-setting to policy evaluation. Gov Inf Q 37(4):101509. https://doi.org/10.1016/j.giq.2020.101509
Vijayakumar V, Balakrishnan N (2021) Artificial intelligence-based agriculture automated monitoring systems using WSN. J Ambient Intell Humaniz Comput 12(7):8009–8016. https://doi.org/10.1007/s12652-020-02530-w
Vinueza-Naranjo PG, Nascimento-Silva HA, Rumipamba-Zambrano R, Ruiz-Gomes I, Rivas-Lalaleo D, Patil NJ (2022) IoT-based smart agriculture and poultry farms for environmental sustainability and development, pp 379–406. https://doi.org/10.1007/978-3-030-75123-4_17
Wang K, Zhao Y, Gangadhari RK, Li Z (2021) Analyzing the adoption challenges of the Internet of Things (Iot) and artificial intelligence (ai) for smart cities in china. Sustainability 13(19):1–35. https://doi.org/10.3390/su131910983
Wankhade N, Kundu GK (2020) Interpretive structural modelling (ISM) methodology and its application in supply chain research. Int J Innovat Technol Explor Eng 9(4):1101–1109. https://doi.org/10.35940/ijitee.d1607.029420
Yahya N (2018) Agricultural 4.0: its implementation toward future sustainability. In: Green Energy and Technology (Vol. 0, Issue 9789811075773). https://doi.org/10.1007/978-981-10-7578-0_5
Zeng Y, Wang L (2017) Fei-Fei Li: artificial intelligence is on its way to reshape the world. Natl Sci Rev. https://doi.org/10.1093/nsr/nwx060
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Appendix 1
Appendix 1
Critical Factors | Paired comparison of OandG Industry Influencers | Yes/No | If yes, then Why and How? |
|---|---|---|---|
Lack of skilled workforce(CF1) | |||
CF1-CF2 | Lack of skilled workforce can Influence High Cost incurred | Yes | As it leads to suboptimal performance by the workforce and the results suffer due to it |
CF1-CF3 | Lack of skilled workforce can influence Lack of computational resource | Yes | As the unskilled workforce won’t be able to use the advanced computational resources |
CF3-CF1 | Lack of computational resource can Influence Lack of skilled workforce | Yes | As the lack computational resources would prevent the workforce from getting skilled |
CF1-CF4 | Lack of skilled workforce can Influence the Extreme climatic conditions | Yes | As the unskilled workforce might not pay attention to the best environment preserving practices |
CF1-CF5 | Lack of skilled workforce can Difficulty in handling data | Yes | As the unskilled workforce would not be able to handle the data |
CF1-CF6 | Lack of skilled workforce can Influence Government policy | Yes | As the government might devise policies for the betterment of the people |
CF6-CF1 | Government policy can influence Lack of skilled workforce | Yes | Better government policies can help in upskilling of the workforce |
CF1-CF7 | Lack of skilled workforce can Influence Reluctance in implementation of advance technology | Yes | As the unskilled workforce would be reluctant to accepting the advancing technologies |
CF7-CF1 | Reluctance in implementation of advance technology can influence Lack of skilled workforce | Yes | Since the advanced technologies would help in upskilling of the workforce |
CF8-CF1 | High precision and accuracy required can Influence Lack of skilled workforce | Yes | As it would allow the workforce to get skilled |
CF1-CF9 | Lack of skilled workforce can Influence Lack in infrastructure | Yes | As the unskilled workforce might not be able to accommodate advanced infrastructure |
CF9-CF1 | Lack in infrastructure can Influence Lack of skilled workforce | Yes | As there won’t be any infrastructure to skill the workforce |
High Cost incurred(CF2) | |||
CF2-CF3 | High Cost incurred can Lack of computational resource | Yes | As the conventional practices are very cost intensive |
CF3-CF2 | Lack of computational resource can Influence High Cost incurred | Yes | As the conventional practices are very cost intensive |
CF4-CF2 | Extreme climatic conditions can Influence High Cost incurred | Yes | As the pre-established methods might have to be changed |
CF2-CF5 | High Cost incurred can Influence Difficulty in handling data | Yes | As the infrastructure for handling the data would be expensive |
CF5-CF2 | Difficulty in handling data can Influence High Cost incurred | Yes | As the conventional practices are cost intensive |
CF2-CF6 | High Cost incurred can Influence Government policy | Yes | As the government can devise policies to reduce the cost and can make the process cheaper |
CF6-CF2 | Government policy can Influence High Cost incurred | Yes | As better government policies might help in reducing the cost |
CF7-CF2 | Reluctance in implementation of advance technology can Influence High Cost incurred | Yes | As the conventional methods might be expensive |
CF2-CF8 | High Cost incurred can Influence High precision and accuracy required | Yes | Better machinery could improve the output |
CF8-CF2 | High precision and accuracy required can Influence High Cost incurred | Yes | As efficient outputs might help in reducing the cost |
CF2-CF9 | High Cost incurred can Influence Lack in infrastructure | Yes | As the improving infrastructure would be expensive |
CF9-CF2 | Lack in infrastrucuture can Influence High Cost incurred | Yes | As the infrastructure might reduce the cost |
Lack of computational resource (CF3) | |||
CF3-CF5 | Lack of computational resource can Influence Difficulty in handling data | Yes | Without computational resources data handling would be difficult |
CF5-CF3 | Difficulty in handling data can Lack of computational resource | Yes | If data handling is difficult then there would be an increase in the computational cost |
CF6-CF3 | Government policy can Lack of computational resource | Yes | Government policies can influence the lack of computational resources |
CF3-CF7 | Lack of computational resource can Influence Reluctance in implementation of advance technology | Yes | As without the computational resources advanced technologies can’t be installed |
CF7-CF3 | Reluctance in implementation of advance technology can Lack of computational resource | Yes | As advanced technology might help in improvement of the computational resources |
CF3-CF8 | Lack of computational resource can Influence High precision and accuracy required | Yes | As less computational resources might give suboptimal results |
CF9-CF3 | Lack in infrastrucuture can Lack of computational resource | Yes | As the improvement in infrastructure might improve the computational resources |
Extreme climatic conditions (CF4) | |||
CF4-CF6 | Extreme climatic conditions can Influence Government policy | Yes | As the government might take some steps towards the changing climate conditions |
CF6-CF4 | Government policy can Influence Extreme climatic conditions | Yes | Appropriate government policies might help in improving the climatic conditions |
CF4-CF7 | Extreme climatic conditions can Influence Reluctance in implementation of advance technology | Yes | As it might reduce the advancement of technology |
CF7-CF4 | Reluctance in implementation of advance technology can Influence Extreme climatic conditions | Yes | As the conventional methods might be harmful for the environment |
CF4-CF8 | Extreme climatic conditions can Influence High precision and accuracy required | Yes | If the working conditions are harsh then the output would be affected |
CF4-CF9 | Extreme climatic conditions can Influence Lack in infrastructure | Yes | As the harsh climatic conditions won’t let people advance the infrastructure |
CF9-CF4 | Lack in infrastructure can Influence Extreme climatic conditions | Yes | As the advancing infrastructure would harm the environment |
Difficulty in handling data (CF5) | |||
CF6-CF5 | Government policy can Influence Difficulty in handling data | Yes | Better IT rules by the government would help in handling the data |
CF5-CF7 | Difficulty in handling data can Influence Reluctance in implementation of advance technology | Yes | As the data handling is a big part of setting up the technological infrastructure |
CF7-CF5 | Reluctance in implementation of advance technology can Influence Difficulty in handling data | Yes | As the advancement in technology will help in better handling of the data |
CF5-CF8 | Difficulty in handling data can Influence High precision and accuracy required | Yes | As poor data handling might lead to suboptimal output |
CF9-CF5 | Lack in infrastructure can Influence Difficulty in handling data | Yes | Advanced technological infrastructure might help in improving the data handling capabilities |
Government policy (CF6) | |||
CF6-CF7 | Government policy can Influence Reluctance in implementation of advance technology | Yes | As the upskilling of the workforce by the government policies can help the people in accepting new technology |
CF7-CF6 | Reluctance in implementation of advance technology can Influence Government policy | Yes | Government might introduce policies to educate the workforce to be more accepting towards the new technology |
CF6-CF8 | Government policy can Influence High precision and accuracy required | Yes | Governmental upskilling might lead to better output by the workforce |
CF6-CF9 | Government policy can Influence Lack in infrastructure | Yes | Extreme government policies can curb the infrastructure development |
CF9-CF6 | Lack in infrastructure can Influence Government policy | Yes | It might push the government to come up with policies which help in developing better infrastructure |
Reluctance in implementation of advance technology (CF7) | |||
CF7-CF8 | Reluctance in implementation of advance technology can Influence High precision and accuracy required | Yes | It might lead to poor output |
CF8-CF7 | High precision and accuracy required can Influence Reluctance in implementation of advance technology | Yes | It might lead to increased use of technology |
CF7-CF9 | Reluctance in implementation of advance technology can Influence Lack in infrastructure | Yes | As it might lead to poor development |
CF9-CF7 | Lack in infrastructure can Influence Reluctance in implementation of advance technology | Yes | As it might not encourage the workforce to take up better technological advancements |
High precision and accuracy required (CF8) | |||
CF9-CF8 | Lack in infrastructure can Influence High precision and accuracy required | Yes | As it might lead to poorer output |
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Hasteer, N., Mallik, A., Nigam, D. et al. Analysis of challenges to implement artificial intelligence technologies in agriculture sector. Int J Syst Assur Eng Manag (2023). https://doi.org/10.1007/s13198-023-02164-z
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DOI: https://doi.org/10.1007/s13198-023-02164-z