Technological Tools Based on Artificial Intelligence in the Sugar Industry: A Bibliometric Analysis and Future Perspectives for Energy Efficiency

Authors

  • Hugo Gaspar Hernandez-Palma Universidad EAN
  • Jonny Rafael Plaza Alvarado Corporación Universitaria Iberoamericana
  • Jesús Enrique García Guiliany Corporación Universitaria Latinoamericana
  • Andrea Liliana Moreno Rios Universidad de la Costa

DOI:

https://doi.org/10.17981/ladee.04.02.2023.4

Keywords:

Technological advancements, sugar industry, artificial intelligence

Abstract

Introduction: The application of Artificial Intelligence –AI– in industrial sugar production, particularly in sensor data and systems management, is rapidly evolving towards real-time monitoring programs that offer valuable recommendations and decision-making support within the sugar industry. Methodology: This comprehensive bibliometric analysis of 125 Scopus-indexed articles highlights significant trends in the field, including surges in article production during 2017, 2018, 2021, and 2022, accounting for 34% of total publications. Results: Scientific production in this domain grew by 3.93% from 1969 to 2023. Most research (81%) originated from key countries, including Australia, Brazil, India, China, the Philippines, the United States, and France. Prominent journals played a pivotal role, representing 19% of publications. Noteworthy authors include Attard, Everingham, Meng, and Sexton, with four published articles each. Remarkably, 88% of researchers in this field are transitory. This study underscores dynamic growth in artificial intelligence applications in sugar production, emphasizing sustainability in data and systems management. Conclusions: The effective integration of these technologies holds the potential to enhance sustainability practices, optimizing efficiency and quality throughout the sugar production supply chain, thereby contributing to the attainment of Sustainable Development Goal 9. The utilization of artificial intelligence to optimize industrial sugar production represents technological innovation capable of improving the efficiency and infrastructure of the sugar industry, consequently fostering global sustainable development.

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References

Andreo-Martínez, P., Ortiz-Martínez, V. M., García-Martínez, N., López, P. P., Quesada-Medina, J., Cámara, M. Á. & Oliva, J. (2020). A descriptive bibliometric study on the bioavailability of pesticides in vegetables, food, or wine research (1976–2018). Environmental Toxicology and Pharmacology, 77(103374), 103374. https://doi.org/10.1016/j.etap.2020.103374

Badshah, M., Lam, D. M., Liu, J. & Mattiasson, B. (2012). Use of an Automatic Methane Potential Test System for evaluating the biomethane potential of sugarcane bagasse after different treatments. Bioresource Technology, 114, 262–269. https://doi.org/10.1016/j.biortech.2012.02.022

Barbosa, R. M., Batista, B. L., Barião, C. V., Varrique, R. M., Coelho, V. A., Campiglia, A. D. & Barbos, F. (2015). Simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry. Food Chemistry, 184, 154–159. https://doi.org/10.1016/j.foodchem.2015.02.146

Barrera, K. V., Pinzón, J. S., Acuña, J. S. & Jiménez-Barbosa, W. G. (2021). Bibliometric analysis of scientific journals related to optometry in Colombia (2014-2019). Revista Salud Bosque, 11(1), 1–20. https://doi.org/10.18270/rsb.v11i1.3412

Bocca, F. F. & Rodrigues, L. (2016). The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modeling. Computers and Electronics in Agriculture, 128, 67–76. https://doi.org/10.1016/j.compag.2016.08.015

Brett, P., McCarthy, A., McCarthy, C., Long, D., Gillies, M., Foley, J. & Baillie, C. (2019, 30 April - 3 May). Advancing automation in the agricultural working environment [Conference]. 41st Annual Conference - Australian Society of Sugar Cane Technologists, ASSCT, Toowoomba, Queensland, Australia. https://www.assct.com.au/conference/past-conferences/172-2019-assct-conference

Chia, M. Y., Huang, Y. F., Koo, C. H. & Fung, K. F. (2020). Recent advances in evapotranspiration estimation using artificial intelligence approaches with a focus on hybridization techniques—a review. Agronomy, 10(1), 1–33. https://doi.org/10.3390/agronomy10010101

De Souza, C. H., Lamparelli, R. A., Rocha, J. & Magalhães, P. S. (2017). Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture, 143, 49–56. https://doi.org/10.1016/j.compag.2017.10.006

Dongre, V. B. & Gandhi, R. S. (2016). Applications of artificial neural networks for enhanced livestock productivity: a review. Indian Journal of Animal Sciences, 86(11), 1232–1237.

El Hajj, M., Bégué, A., Guillaume, S. & Martiné, J.-F. (2009). Integrating SPOT-5 time series, crop growth modeling, and expert knowledge for monitoring agricultural practices — The case of sugarcane harvest on Reunion Island. Remote Sensing of Environment, 113(10), 2052–2061. https://doi.org/10.1016/j.rse.2009.04.009

Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377–4383. https://doi.org/10.48084/etasr.2756

Evans, K. J., Terhorst, A. & Kang, B. H. (2017). From data to decisions: helping crop producers build their actionable knowledge. Critical reviews in plant sciences, 36(2), 71–88. https://doi.org/10.1080/07352689.2017.1336047

Gillies, M., Attard, S., Jaramillo, A., Davis, M. & Foley, J. (2017, 3-5 May). Smart automation of furrow irrigation in the sugar industry [Conference]. 39th Conference of the Australian Society of Sugar Cane Technologists, ASSCT, Cairns City, Australia. https://www.assct.com.au/conference/past-conferences/112-2017-assct-conference

Godoi, A. F. L., Ravindra, K., Godoi, R. H. M., Andrade, S. J., Santiago-Silva, M., Van Vaeck, L. & Van Grieken, R. (2004). Fast chromatographic determination of polycyclic aromatic hydrocarbons in aerosol samples from sugar cane burning. Journal of Chromatography A, 1027(1–2), 49–53. https://doi.org/10.1016/j.chroma.2003.10.048

Goswami, L., Kayalvizhi, R., Dikshit, P. K., Sherpa, K. C., Roy, S., Kushwaha, A., Kim, B. S., Banerjee, R., Jacob, S. & Rajak, R. C. (2022). A critical review on prospects of bio-refinery products from second and third-generation biomasses. Chemical Engineering Journal, 448, 137677. https://doi.org/10.1016/j.cej.2022.137677

Gozá, O., De Alejo, H. E. & Rijckaert, M. (2002). Use of Simulation and Expert Systems to increase the energy efficiency in cane sugar factories. Developments in Chemical Engineering and Mineral Processing, 10(1-2), 165–179. https://dx.doi.org/10.1002/apj.5500100112

Ihaka, R. & Gentleman, R. (2023). R (version 4.3.1). R Development Core Team. https://www.r-project.org/

Iturralde, L. A., Bambi, E. & Espinosa, A. R. (2021). Methodology for sugar energy balance of power stations. LADEE, 2(2), 1–15. https://doi.org/10.17981/ladee.02.02.2022.01

Jha, K., Doshi, A., Patel, P. & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12. https://doi.org/10.1016/j.aiia.2019.05.004

Jonker, J. G. G., van der Hilst, F., Junginger, H. M., Cavalett, O., Chagas, M. F. & Faaij, A. P. C. (2015). Outlook for ethanol production costs in Brazil up to 2030, for different biomass crops and industrial technologies. Applied Energy, 147, 593–610. https://doi.org/10.1016/j.apenergy.2015.01.090

Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A. & Chau, K.-W. (2019). Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. The Science of the Total Environment, 664, 1005–1019. https://doi.org/10.1016/j.scitotenv.2019.02.004

Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H. & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033

Kantasa-Ard, A., Bekrar, A., Ait El Cad, A. & Sallez, Y. (2019). Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand. IFAC-PapersOnLine, 52(13), 725–730. https://doi.org/10.1016/j.ifacol.2019.11.201

Kapoor, M., Panwar, D. & Kaira, G. S. (2016). Bioprocesses for enzyme production using Agro-industrial wastes. In G. S. Dhillon and S. Kaur, Agro-Industrial Wastes as Feedstock for Enzyme Production (pp. 61–93). Elsevier. https://doi.org/10.1016/B978-0-12-802392-1.00003-4

Karri, R. R., Sahu, J. N. & Meikap, B. C. (2020). Improving the efficacy of the Cr (VI) adsorption process on sustainable adsorbent derived from waste biomass (sugarcane bagasse) with the help of ant colony optimization. Industrial Crops and Products, 143(111927), 111927. https://doi.org/10.1016/j.indcrop.2019.111927

Koulouris, A., Misailidis, N. & Petrides, D. (2021). Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, 317–333. https://doi.org/10.1016/j.fbp.2021.01.016

Le Blond, J. S., Williamson, B. J., Horwell, C. J., Monro, A. K., Kirk, C. A. & Oppenheimer, C. (2008). Production of potentially hazardous respirable silica airborne particulate from the burning of sugarcane. Atmospheric Environment, 42(22), 5558–5568. https://doi.org/10.1016/j.atmosenv.2008.03.018

Leidien University. (2019). VOSviewer (version 1.6.19). https://www.vosviewer.com/

Luciano, A. C., Picoli, M. C., Rocha, J., Franco, H., Sanches, G., Leal, M., & Le Maire, G. (2018). Generalized space-time classifiers for monitoring sugarcane areas in Brazil. Remote sensing of environment, 215, 438–451. https://doi.org/10.1016/j.rse.2018.06.017

Maldaner, F. L., Corrêdo, L., Canata, T. F. & Molin, J. P. (2021). Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, 181(105945), 105945. https://doi.org/10.1016/j.compag.2020.105945

Martín-del-Río, B., Neipp, M.-C., García-Selva, A. & Solanes-Puchol, A. (2021). Positive organizational psychology: A bibliometric review and science mapping analysis. International Journal of Environmental Research and Public Health, 18(10), 1–17. https://doi.org/10.3390/ijerph18105222

Muir, A., Harrison, E. & Wheals, A. (2011). A multiplex set of species-specific primers for rapid identification of members of the genus Saccharomyces. FEMS yeast research, 11(7), 552–563. https://doi.org/10.1111/j.1567-1364.2011.00745.x

Natarajan, R., Subramanian, J. & Papageorgiou, E. I. (2016). Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Computers and Electronics in Agriculture, 127, 147–157. https://doi.org/10.1016/j.compag.2016.05.016

Patrício, D. I. & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and electronics in agriculture, 153, 69–81. https://doi.org/10.1016/j.compag.2018.08.001

Ridge, R. (2003). Trends in sugar cane mechanization. International Sugar Journal, 105(1252), 150–154. https://internationalsugarjournal.com/

Rivera, E., Rabelo, S., Garcia, D., Maciel Filho, R. & da Costa, A. (2010). Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks. Journal of Chemical Technology and Biotechnology, 85(7), 983–992. https://doi.org/10.1002/jctb.2391

Rivera, E., Costa, A., Andrade, R., Atala, D., Maugeri, F. & Filho, R. (2007). Development of adaptive modeling techniques to describe the temperature-dependent kinetics of biotechnological processes. Biochemical Engineering Journal, 36(2), 157–166. https://doi.org/10.1016/j.bej.2007.02.011

Rooh, U. A., Li, A., & Ali, M. M. (2015). Fuzzy, neural network and expert systems methodologies and applications-a review. Journal of Mobile Multimedia, 11(1-2), 157–176. https://dl.acm.org/doi/abs/10.5555/2871240.2871253

Shaikh, T. A., Rasool, T. & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198(C), 107119. https://doi.org/10.1016/j.compag.2022.107119

Solarte-Toro, J. C. & Cardona, C. A. (2023). Sustainability of Biorefineries: Challenges and Perspectives. Energies, 16(9), 1–29. https://doi.org/10.3390/en16093786

Stuurman, K. & Lachaud, E. (2022). Regulating AI. A label to complete the proposed Act on Artificial Intelligence. Computer Law & Security Review, 44, 1–23. https://doi.org/10.1016/j.clsr.2022.105657

Stray, B. J., van Vuuren, J. H. & Bezuidenhout, C. N. (2012). An optimization-based seasonal sugarcane harvest scheduling decision support system for commercial growers in South Africa. Computers and Electronics in Agriculture, 83, 21–31. https://doi.org/10.1016/j.compag.2012.01.009

Talaviya, T., Shah, D., Patel, N., Yagnik, H. & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73. https://doi.org/10.1016/j.aiia.2020.04.002

Verdouw, C. N., Wolfert, J., Beulens, A. J. M. & Rialland, A. (2016). Virtualization of food supply chains with the Internet of Things. Journal of Food Engineering, 176, 128–136. https://doi.org/10.1016/j.jfoodeng.2015.11.009

Vieira, M. A., Formaggio, A. R., Rennó, C. D., Atzberger, C., Aguiar, D. A. & Mello, M. P. (2012). Object-Based Image Analysis and Data Mining applied to a remotely sensed Landsat time series to map sugarcane over large areas. Remote Sensing of Environment, 123, 553–562. https://doi.org/10.1016/j.rse.2012.04.011

Vitcosque, G. L., Fonseca, R. F., Rodríguez-Zúñiga, U. F., Bertucci.Neto, V., Couri, S. & Farinas, C. S. (2012). Production of biomass-degrading multienzyme complexes under solid-state fermentation of soybean meal using a bioreactor. Enzyme Research, 1–10. https://doi.org/10.1155/2012/248983

Wang, E., Attard, S., Everingham, Y., Philippa, B. & Xiang, W. (2018, 18-20 April). Smarter irrigation management in the sugarcane farming system using the Internet of Things [Conference]. 40th Annual Conference Australian Society of Sugar Cane Technologists, ASSCT, Mackay, Australia. https://www.assct.com.au/conference/past-conferences/153-2018-assct-conference

Wang, E., Attard, S., Linton, A., McGlinchey, M., Xiang, W., Philippa, B. & Everingham, Y. (2020). Development of a closed-loop irrigation system for sugarcane farms using the Internet of Things. Computers and Electronics in Agriculture, 172(105376), 105376. https://doi.org/10.1016/j.compag.2020.105376

West, N. (2021, 20-23 April). Management of the technical skillset required to support automation in the sugar industry [Conference]. 42nd Australian Society of Sugar Cane Technologists Conference 2021, ASSCT, Bundaberg, Australia. https://www.proceedings.com/australian-society-of-sugar-cane-technologists-assct/

Yoosefzadeh-Najafabadi, M., Hesami, M. & Eskandari, M. (2023). Machine learning-assisted approaches in modernized plant breeding programs. Genes, 14(4), 1–22. https://doi.org/10.3390/genes14040777

Zafar, M., Kumar, S., Kumar, S. & Dhiman, A. K. (2012). Artificial intelligence-based modeling and optimization of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production process by using Azohydromonas lata MTCC 2311 from cane molasses supplemented with volatile fatty acids: A genetic algorithm paradigm. Bioresource Technology, 104, 631–641. https://doi.org/10.1016/j.biortech.2011.10.024

Zhang, J., Meng, Y., Wu, J., Qin, J., Wang, H., Yao, T. & Yu, S. (2020). Monitoring sugar crystallization with deep neural networks. Journal of Food Engineering, 280(109965), 109965. https://doi.org/10.1016/j.jfoodeng.2020.109965.

Published

2023-12-17

How to Cite

Hernandez-Palma, H. G., Plaza Alvarado, J. R., García Guiliany, J. E., & Moreno Rios, A. L. (2023). Technological Tools Based on Artificial Intelligence in the Sugar Industry: A Bibliometric Analysis and Future Perspectives for Energy Efficiency . LADEe Latin American Developments in Energy Engineering, 4(2), 49–64. https://doi.org/10.17981/ladee.04.02.2023.4