Abstract
By 2050, the world’s population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-industrial wastes are rich in bioactive substances and other medicinal properties. They can be used as a different source for manufacturing products like biogas, biofuels, mushrooms, and tempeh, the primary ingredients in various studies and businesses. Increased importance is placed on resource recovery, recycling, and reusing (RRR) any waste using advanced technology like IoT and artificial intelligence. AI algorithms offer alternate, creative methods for managing agro-industrial waste management (AIWM). There are contradictions and a need to understand how AI technologies work regarding their application to AIWM. This research studies the application of AI-based technology for the various areas of AIWM. The current work aims to discover AI-based models for forecasting the generation and recycling of AIWM waste. Research shows that agro-industrial waste generation has increased worldwide. Infrastructure needs to be upgraded and improved by adapting AI technology to maintain a balance between socioeconomic structures. The study focused on AI’s social and economic impacts and the benefits, challenges, and future work in AIWM. The present research will increase recycling and reproduction with a balance of cost, efficiency, and human resources consumption in agro-industrial waste management.
Similar content being viewed by others
Data Availability
Not applicable.
References
Agarwal V, Shivam G, Sanskriti G (2020) Artificial intelligence in waste electronic and electrical equipment treatment opportunities and challenges. In 2020 International Conference on Intelligent Engineering and Management (ICIEM), London, UK, 526–529. https://doi.org/10.1109/ICIEM48762.2020.9160065
Ahuja I, Dauksas E, Remme JF, Richardsen R, Loes AK (2020) Fish and fish waste-based fertilizers in organic farming – with status in Norway: a review. Waste Manag 115:95–112. https://doi.org/10.1016/j.wasman.2020.07.025
Alshater H, Yasmine SM, Ibrahim ET (2023) The impact of artificial intelligence on waste management for climate change. In The power of data driving climate change with data science and artificial intelligence innovations. Volume 118, Cham Springer Nature Switzerland, pp 39–59. https://doi.org/10.1007/978-3-031-22456-0
Arvanitoyannis IS, Kassaveti A (2008) Fish industry waste: treatments environmental impacts current and potential uses. Int J Food Sci Technol 43:726–745. https://doi.org/10.1111/j.1365-2621.2006.01513.x
Chowdhury, TA, Nusrat JS, Sajid HS, Md TH, Mubassir H, Rashedur MR (2022) Object detection based management system of solid waste using artificial intelligence techniques. In 2022, IEEE 13th annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). New York, NY, NY, USA, pp 0019–0023. https://doi.org/10.1109/UEMCON54665.2022.9965643
Edwards W, Duffy P (2014) Farm management. Encyclopaedia of agriculture and food Systems, 2nd Edn. pp 100–112. https://doi.org/10.1016/B978-0-444-52512-3.00111-X
Jude AB, Deepmala S, Saiful IMJ, Sandeep S, Prabha B, Pravin RK (2022) An artificial intelligence based predictive approach for smart waste management. Wirel Pers Commun 127:15–16. https://doi.org/10.1007/s11277-021-08803-7
Lynda A, Santoso W, Srimannarayana G (2022) Artificial intelligence applications for sustainable solid waste management practices in Australia: a systematic review. Sci Total Environ 834. https://doi.org/10.1016/j.scitotenv.2022.155389
Muscolo A, Mauriello F, Marra F, Calabro PS, Russo M, Ciriminna R, Pagliaro M, Fert A (2022) A new organic fertilizer from fish processing waste for sustainable agriculture. Glob Chall. https://doi.org/10.1002/gch2.202100141
Patel GS, Rai A, Das NN, Singh RP (2021) Smart agriculture: emerging pedagogies of deep learning. machine learning and internet of things. CRC Press, London, 1st edition. https://doi.org/10.1201/b22627
Pravin RK, Neeraj K, Ahmed HA, Fawaz A, Asif IK, Saiful I, Jyoti PR, Kenenisa D (2022) Artificial intelligence-based robotic technique for reusable waste materials. Comput Intell Neurosci. https://doi.org/10.1155/2022/2073482
Rai A, Mishra AR (2022) Deep learning in smart agriculture applications. In Artificial intelligence. 1st Edition. Chapman and Hall/CRC, New York, pp 165–184. https://doi.org/10.1201/9781003217237
Rai A, Prakash OM (2022) Application of machine learning in agriculture with some examples. In Artificial Intelligence, 1st Edition. Chapman and Hall/CRC, New York, pp 139–163. https://doi.org/10.1201/9781003217237
Rayda BA, Mohsen H (2021) Artificial intelligence to improve the food and agriculture sector. Hindawi J Food Qual 2021:7. https://doi.org/10.1155/2021/5584754
Ryan M (2023) The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature. AI & SOCIETY 38:2473–2485. https://doi.org/10.1007/s00146-021-01377-9
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study’s conception and design. Amrita Rai performed graphics and table preparation, data collection, and interpretation. Amrita Rai and Krishan Kundu wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Ta Yeong Wu
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Rai, A., Kundu, K. Agro-industrial waste management employing benefits of artificial intelligence. Environ Sci Pollut Res 31, 33148–33154 (2024). https://doi.org/10.1007/s11356-024-33526-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-024-33526-0