The readiness to adopt green intelligent and sustainable manufacturing for agriculture in industry 4.0

Muhammad Yahya Hammad; Muhammad Ashraf Fauzi; Puteri Fadzline Muhamad Tamyez; Ahmad Nazif Noor Kamar; Syed Radzi Rahamaddulla; Muhammad Yahya Hammad; Muhammad Ashraf Fauzi; Puteri Fadzline Muhamad Tamyez; Ahmad Nazif Noor Kamar; Syed Radzi Rahamaddulla

doi:10.3934/environsci.2025030

AIMS Environmental Science

2025, Volume 12, Issue 4: 682-702. doi: 10.3934/environsci.2025030

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Research article Special Issues

The readiness to adopt green intelligent and sustainable manufacturing for agriculture in industry 4.0

Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah

Received: 17 February 2025 Revised: 18 June 2025 Accepted: 24 June 2025 Published: 28 July 2025

The desire to balance environmental sustainability and food security is causing a significant upheaval in the agricultural sector. This study investigated how prepared stakeholders are to embrace agriculture in Industry 4.0, which integrates green, intelligent, and sustainable technologies like blockchain, IoT, and AI. Although these advances promise increased resilience and efficiency, they present significant obstacles to implementation, such as inadequate infrastructure, socioeconomic inequality, and a lack of technology awareness. This study highlights these technologies' theoretical and managerial implications and finds critical gaps using bibliometric and thematic analysis. To overcome adoption obstacles, it emphasizes the significance of inclusive policies, strategic alliances, and capacity-building programs. The study concludes that these impediments must be addressed to secure food systems for future generations and align agriculture with global sustainability goals. Longitudinal studies, stakeholder-centric methodologies, and investigating emergent technology synergies are among the suggestions for future research.
- artificial intelligence,
- machine learning,
- industry 4.0,
- smart manufacturing,
- green intelligence,
- sustainable manufacturing,
- agriculture,
- bibliometric analysis
Citation: Muhammad Yahya Hammad, Muhammad Ashraf Fauzi, Puteri Fadzline Muhamad Tamyez, Ahmad Nazif Noor Kamar, Syed Radzi Rahamaddulla. The readiness to adopt green intelligent and sustainable manufacturing for agriculture in industry 4.0[J]. AIMS Environmental Science, 2025, 12(4): 682-702. doi: 10.3934/environsci.2025030

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Abstract

The desire to balance environmental sustainability and food security is causing a significant upheaval in the agricultural sector. This study investigated how prepared stakeholders are to embrace agriculture in Industry 4.0, which integrates green, intelligent, and sustainable technologies like blockchain, IoT, and AI. Although these advances promise increased resilience and efficiency, they present significant obstacles to implementation, such as inadequate infrastructure, socioeconomic inequality, and a lack of technology awareness. This study highlights these technologies' theoretical and managerial implications and finds critical gaps using bibliometric and thematic analysis. To overcome adoption obstacles, it emphasizes the significance of inclusive policies, strategic alliances, and capacity-building programs. The study concludes that these impediments must be addressed to secure food systems for future generations and align agriculture with global sustainability goals. Longitudinal studies, stakeholder-centric methodologies, and investigating emergent technology synergies are among the suggestions for future research.

References

[1]	Mukhopadhyay SS (2014) Nanotechnology in agriculture: prospects and constraints. Nanotechnol Sci Appl 63. https://doi.org/10.2147/NSA.S39409 doi: 10.2147/NSA.S39409
[2]	Liakos K, Busato P, Moshou D, et al. (2018) Machine Learning in Agriculture: A Review. Sensors 18: 2674. https://doi.org/10.3390/s18082674 doi: 10.3390/s18082674
[3]	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. https://doi.org/10.1016/j.compag.2017.09.037 doi: 10.1016/j.compag.2017.09.037
[4]	Tester M, Langridge P (2010) Breeding Technologies to Increase Crop Production in a Changing World. Science 327: 818–822. https://doi.org/10.1126/science.1183700 doi: 10.1126/science.1183700
[5]	Rejeb A, Rejeb K, Abdollahi A, et al. (2022) The Interplay between the Internet of Things and agriculture: A bibliometric analysis and research agenda. Internet Things (Netherlands) 19: 100580. https://doi.org/10.1016/j.iot.2022.100580 doi: 10.1016/j.iot.2022.100580
[6]	Sun H, Xiong S, Shi B, et al. (2024) Flexible Surface-Enhanced Raman Scattering (SERS) sensor for residue-free pesticide detection based on agriculture 4.0 concepts. Colloids Surf A Physicochem Eng Asp 700: 134647. https://doi.org/10.1016/j.colsurfa.2024.134647 doi: 10.1016/j.colsurfa.2024.134647
[7]	Wan Y, Wei Q, Sun H, et al. (2025) Machine learning assisted biomimetic flexible SERS sensor from seashells for pesticide classification and concentration prediction. Chem Eng J 507: 160813. https://doi.org/10.1016/j.cej.2025.160813 doi: 10.1016/j.cej.2025.160813
[8]	Chen B, Shi B, Gong J, et al. (2024) Quality detection and variety classification of pecan seeds using hyperspectral imaging technology combined with machine learning. J Food Compos Anal 131: 106248. https://doi.org/10.1016/j.jfca.2024.106248 doi: 10.1016/j.jfca.2024.106248
[9]	Piñeiro V, Arias J, Dürr J, et al. (2020) A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat Sustain 3: 809–820. https://doi.org/10.1038/s41893-020-00617-y doi: 10.1038/s41893-020-00617-y
[10]	Jellason NP, Robinson EJZ, Ogbaga CC (2021) Agriculture 4.0: Is Sub-Saharan Africa Ready? Appl Sci 11: 5750. https://doi.org/10.3390/app11125750 doi: 10.3390/app11125750
[11]	Onyeaka H, Tamasiga P, Nwauzoma UM, et al. (2023) Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review. Sustainability 15: 10482. https://doi.org/10.3390/su151310482 doi: 10.3390/su151310482
[12]	Klerkx L, Begemann S (2020) Supporting food systems transformation: The what, why, who, where and how of mission-oriented agricultural innovation systems. Agric Syst 184: 102901. https://doi.org/10.1016/j.agsy.2020.102901 doi: 10.1016/j.agsy.2020.102901
[13]	Ramírez-Moreno MA, Keshtkar S, Padilla-Reyes DA, et al. (2021) Sensors for Sustainable Smart Cities: A Review. Appl Sci 11: 8198. https://doi.org/10.3390/app11178198 doi: 10.3390/app11178198
[14]	Fargnoli M, Parrella E, Costantino F, et al. (2024) Hybrid solutions for agricultural vehicles: A comparative life cycle analysis from the users' standpoint. J Clean Prod 485: 144406. https://doi.org/10.1016/j.jclepro.2024.144406 doi: 10.1016/j.jclepro.2024.144406
[15]	Javeed B, Ridwan Q, Huang D, et al. (2024) Ecological niche modelling: a global assessment based on bibliometric analysis. Front Environ Sci 12. https://doi.org/10.3389/fenvs.2024.1376213 doi: 10.3389/fenvs.2024.1376213
[16]	Ng JY, Liu H, Masood M, et al. (2024) Guidance for the Reporting of Bibliometric Analyses: A Scoping Review. Quant Sci Stud https://doi.org/10.1101/2024.08.26.24312538 doi: 10.1101/2024.08.26.24312538
[17]	Vigoroso L, Caffaro F, Tronci M, et al. (2025) Ergonomics and design for safety: A scoping review and bibliometric analysis in the industrial engineering literature. Saf Sci 185: 106799. https://doi.org/10.1016/j.ssci.2025.106799 doi: 10.1016/j.ssci.2025.106799
[18]	Nwagwu WE (2024) Bibliographic coupling networks of global research on data literacy by documents, sources and authors. J Libr Inf Sci https://doi.org/10.1177/09610006241252655 doi: 10.1177/09610006241252655
[19]	Kleminski R, Kazienko P, Kajdanowicz T (2022) Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification. J Inf Sci 48: 349–373. https://doi.org/10.1177/0165551520962775 doi: 10.1177/0165551520962775
[20]	Kumar V, Srivastava A (2023) Mapping the evolution of research themes in business ethics: a co-word network analysis. VINE J Inf Knowl Manag Syst 53: 491–522. https://doi.org/10.1108/VJIKMS-10-2020-0199 doi: 10.1108/VJIKMS-10-2020-0199
[21]	Dwivedi R, Nerur S, Balijepally V (2023) Exploring artificial intelligence and big data scholarship in information systems: A citation, bibliographic coupling, and co-word analysis. Int J Inform Manage Data Insights 3: 100185. https://doi.org/10.1016/j.jjimei.2023.100185 doi: 10.1016/j.jjimei.2023.100185
[22]	Cobo MJ, López-Herrera AG, Herrera-Viedma E, et al. (2011) Science mapping software tools: Review, analysis, and cooperative study among tools. J Am Soc Inf Sci Technol 62: 1382–1402. https://doi.org/10.1002/asi.21525 doi: 10.1002/asi.21525
[23]	van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84: 523–538. https://doi.org/10.1007/s11192-009-0146-3 doi: 10.1007/s11192-009-0146-3
[24]	Alzubaidi L, Zhang J, Humaidi AJ, et al. (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8: 53. https://doi.org/10.1186/s40537-021-00444-8 doi: 10.1186/s40537-021-00444-8
[25]	Fauzi MA, Mohd Ali NS, Mat Russ N, et al. (2024) Halal certification in food products: science mapping of present and future trends. J Islamic Mark 15: 3564–3580. https://doi.org/10.1108/JIMA-12-2023-0407 doi: 10.1108/JIMA-12-2023-0407
[26]	Allioui H, Mourdi Y (2023) Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey. Sensors 23: 8015. https://doi.org/10.3390/s23198015 doi: 10.3390/s23198015
[27]	Samizadeh Nikoui T, Rahmani AM, Balador A, et al. (2021) Internet of Things architecture challenges: A systematic review. Int J Commun Syst 34. https://doi.org/10.1002/dac.4678 doi: 10.1002/dac.4678
[28]	Araújo SO, Peres RS, Barata J, et al. (2021) Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities. Agronomy 11: 667. https://doi.org/10.3390/agronomy11040667 doi: 10.3390/agronomy11040667
[29]	Villa-Henriksen A, Edwards GTC, Pesonen LA, et al. (2020) Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosyst Eng 191: 60–84. https://doi.org/10.1016/j.biosystemseng.2019.12.013 doi: 10.1016/j.biosystemseng.2019.12.013
[30]	Ali O, Ishak MK, Bhatti MKL, et al. (2022) A Comprehensive Review of Internet of Things: Technology Stack, Middlewares, and Fog/Edge Computing Interface. Sensors 22: 995. https://doi.org/10.3390/s22030995 doi: 10.3390/s22030995
[31]	Abbasi R, Martinez P, Ahmad R (2022) The digitization of agricultural industry – a systematic literature review on agriculture 4.0. Smart Agric Technol 2: 100042. https://doi.org/10.1016/j.atech.2022.100042 doi: 10.1016/j.atech.2022.100042
[32]	Sharma V, Tripathi AK, Mittal H (2022) Technological revolutions in smart farming: Current trends, challenges & future directions. Comput Electron Agric 201: 107217. https://doi.org/10.1016/j.compag.2022.107217 doi: 10.1016/j.compag.2022.107217
[33]	Da Silveira F, Lermen FH, Amaral FG (2021) An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages. Comput Electron Agric 189: 106405. https://doi.org/10.1016/j.compag.2021.106405 doi: 10.1016/j.compag.2021.106405
[34]	Dayioğlu MA, Türker U (2021) Digital Transformation for Sustainable Future - Agriculture 4.0: A review. J Agri Sci 27: 373–399. https://doi.org/10.15832/ankutbd.986431 doi: 10.15832/ankutbd.986431
[35]	Bongomin O, Yemane A, Kembabazi B, et al. (2020) Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art. J Eng 2020. https://doi.org/10.20944/preprints202006.0007.v1
[36]	Ronaghi MH (2021) A blockchain maturity model in agricultural supply chain. Inf Process Agric 8: 398–408. https://doi.org/10.1016/j.inpa.2020.10.004 doi: 10.1016/j.inpa.2020.10.004
[37]	Vijayakumar MD, Surendhar GJ, Natrayan L, et al. (2022) Evolution and Recent Scenario of Nanotechnology in Agriculture and Food Industries. J Nanomater 2022. https://doi.org/10.1155/2022/1280411
[38]	Bellantuono N, Nuzzi A, Pontrandolfo P, et al. (2021) Digital transformation models for the i4.0 transition: Lessons from the change management literature. Sustainability (Switzerland) 13. https://doi.org/10.3390/su132312941 doi: 10.3390/su132312941
[39]	Verdejo Espinosa Á, Lopez Ruiz JL, Mata Mata F, et al. (2021) Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals. Sensors 21: 2330. https://doi.org/10.3390/s21072330 doi: 10.3390/s21072330
[40]	Leal Filho W, Wall T, Rui Mucova SA, et al. (2022) Deploying artificial intelligence for climate change adaptation. Technol Forecast Soc Change 180: 121662. https://doi.org/10.1016/j.techfore.2022.121662 doi: 10.1016/j.techfore.2022.121662
[41]	Rolandi S, Brunori G, Bacco M, et al. (2021) The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts. Sustainability 13: 5172. https://doi.org/10.3390/su13095172 doi: 10.3390/su13095172
[42]	Amentae TK, Gebresenbet G (2021) Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications. Sustainability 13: 12181. https://doi.org/10.3390/su132112181 doi: 10.3390/su132112181
[43]	Almalki FA, Soufiene BO, Alsamhi SH, et al. (2021) A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs. Sustainability 13: 5908. https://doi.org/10.3390/su13115908 doi: 10.3390/su13115908
[44]	Anderson NT, Walsh KB, Wulfsohn D (2021) Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time. Agronomy 11: 1409. https://doi.org/10.3390/agronomy11071409 doi: 10.3390/agronomy11071409
[45]	Amiri M, Tofigh F, Shariati N, et al. (2021) Review on Metamaterial Perfect Absorbers and Their Applications to IoT. IEEE Internet Things J 8: 4105–4131. https://doi.org/10.1109/JIOT.2020.3025585 doi: 10.1109/JIOT.2020.3025585
[46]	Ahmad I, Yang Y, Yue Y, et al. (2022) Deep Learning Based Detector YOLOv5 for Identifying Insect Pests. Appl Sci 12: 10167. https://doi.org/10.3390/app121910167 doi: 10.3390/app121910167
[47]	Antony AP, Leith K, Jolley C, et al. (2020) A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture. Sustainability 12: 3750. https://doi.org/10.3390/su12093750 doi: 10.3390/su12093750
[48]	Ferrag MA, Shu L, Friha O, et al. (2022) Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions. IEEE/CAA J Automatica Sin 9: 407–436. https://doi.org/10.1109/JAS.2021.1004344 doi: 10.1109/JAS.2021.1004344
[49]	Aldy JE, Hrubovcak J, Vasavada U (1998) The role of technology in sustaining agriculture and the environment. Ecol Econ 26: 81–96. https://doi.org/10.1016/S0921-8009(97)00068-2 doi: 10.1016/S0921-8009(97)00068-2
[50]	Bechar A, Vigneault C (2016) Agricultural robots for field operations: Concepts and components. Biosyst Eng 149: 94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014 doi: 10.1016/j.biosystemseng.2016.06.014
[51]	Singh G, Kalra N, Yadav N, et al. (2022) Smart Agriculture: a Review. Siberian Journal of Life Sciences and Agriculture 14: 423–454. https://doi.org/10.12731/2658-6649-2022-14-6-423-454 doi: 10.12731/2658-6649-2022-14-6-423-454
[52]	Khan N, Ray RL, Sargani GR, et al. (2021) Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability (Switzerland) 13: 1–31. https://doi.org/10.3390/su13094883 doi: 10.3390/su13094883
[53]	Khanal S, Kushal KC, Fulton JP, et al. (2020) Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sens (Basel) 12: 1–29. https://doi.org/10.3390/rs12223783 doi: 10.3390/rs12223783
[54]	Verma KK, Song XP, Joshi A, et al. (2022) Recent Trends in Nano-Fertilizers for Sustainable Agriculture under Climate Change for Global Food Security. Nanomaterials 12: 1–25. https://doi.org/10.3390/nano12010173 doi: 10.3390/nano12010173
[55]	Nhemachena C, Nhamo L, Matchaya G, et al. (2020) Climate change impacts on water and agriculture sectors in southern africa: Threats and opportunities for sustainable development. Water (Switzerland) 12: 1–17. https://doi.org/10.3390/w12102673 doi: 10.3390/w12102673
[56]	Yokamo S (2020) Adoption of Improved Agricultural Technologies in Developing Countries: Literature Review. Int J Food Sci Agri 4: 183–190. https://doi.org/10.26855/ijfsa.2020.06.010 doi: 10.26855/ijfsa.2020.06.010
[57]	Shah A, Nazari M, Antar M, et al. (2021) PGPR in Agriculture: A Sustainable Approach to Increasing Climate Change Resilience. Front Sustain Food Syst 5: 1–22. https://doi.org/10.3389/fsufs.2021.667546 doi: 10.3389/fsufs.2021.667546
[58]	Bolfe ÉL, Jorge LA de C, Sanches ID, et al. (2020) Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture (Switzerland) 10: 1–16. https://doi.org/10.3390/agriculture10120653 doi: 10.3390/agriculture10120653
[59]	Benos L, Tagarakis AC, Dolias G, et al. (2021) Machine learning in agriculture: A comprehensive updated review. Sensors 21: 1–55. https://doi.org/10.3390/s21113758 doi: 10.3390/s21113758
[60]	Coulibaly S, Kamsu-Foguem B, Kamissoko D, et al. (2022) Deep learning for precision agriculture: A bibliometric analysis. Intell Syst Appl 16: 200102. https://doi.org/10.1016/j.iswa.2022.200102 doi: 10.1016/j.iswa.2022.200102
[61]	Sharma A, Jain A, Gupta P, et al. (2021) Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access 9: 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415 doi: 10.1109/ACCESS.2020.3048415
[62]	Jha K, Doshi A, Patel P, et al. (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artif Intell Agric 2: 1–12. https://doi.org/10.1016/j.aiia.2019.05.004 doi: 10.1016/j.aiia.2019.05.004
[63]	Tudi M, Ruan HD, Wang L, et al. (2021) Tudi2021.Pdf. Environ Res Pub Health 18: 1–23. https://doi.org/10.3390/ijerph18031112 doi: 10.3390/ijerph18031112
[64]	Benitez-Alfonso Y, Soanes BK, Zimba S, et al. (2023) Enhancing climate change resilience in agricultural crops. Curr Biol 33: R1246–R1261. https://doi.org/10.1016/j.cub.2023.10.028 doi: 10.1016/j.cub.2023.10.028
[65]	Hooks D, Davis Z, Agrawal V, et al. (2022) Exploring factors influencing technology adoption rate at the macro level: A predictive model. Technol Soc 68: 101826. https://doi.org/10.1016/j.techsoc.2021.101826 doi: 10.1016/j.techsoc.2021.101826
[66]	Morchid A, El Alami R, Raezah AA, et al. (2024) Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges. Ain Shams Engineering J 15: 102509. https://doi.org/10.1016/j.asej.2023.102509 doi: 10.1016/j.asej.2023.102509
[67]	Borim-de-Souza R, Travis EF, Jan-Chiba JHF, et al. (2023) CROSS-SECTOR PARTNERSHIPS & SUSTAINABLE DEVELOPMENT: COUNTER-ARGUING OPTIMISM. Revista de Administração de Empresas 63. https://doi.org/10.1590/s0034-759020230307x doi: 10.1590/s0034-759020230307x
[68]	Adebunmi Okechukwu Adewusi, Njideka Rita Chiekezie, Nsisong Louis Eyo-Udo (2022) The role of AI in enhancing cybersecurity for smart farms. World J Adv Res Rev 15: 501–512. https://doi.org/10.30574/wjarr.2022.15.3.0889 doi: 10.30574/wjarr.2022.15.3.0889
[69]	Alam MJ, Sarma PK, Begum IA, et al. (2024) Agricultural extension service, technology adoption, and production risk nexus: Evidence from Bangladesh. Heliyon 10: e34226. https://doi.org/10.1016/j.heliyon.2024.e34226 doi: 10.1016/j.heliyon.2024.e34226

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