Applications of Artificial Intelligence in the Sugar Industry: The Past, Present, and Future

Authors

  • Kingsley O. Iwuozor Factory Operations Department, Nigeria Sugar Institute, Ilorin, Nigeria; Faculty of Physical Sciences, Department of Industrial Chemistry, University of Ilorin, Ilorin, Nigeria; Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Nigeria Author
  • Ejidike Lynda Chinyere Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Nigeria Author
  • John C. Iwuozor Department of Industrial Mathematics, Nnamdi Azikiwe University, Awka, Nigeria Author
  • Ebuka Chizitere Emenike Faculty of Physical Sciences, Department of Industrial Chemistry, University of Ilorin, Ilorin, Nigeria; Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Nigeria Author
  • Abel U. Egbemhenghe Department of Chemistry and Biochemistry, College of Art and Science, Texas Tech University, Lubbock, USA Author
  • Marycynthia Ebere Amadi Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Nigeria Author
  • Adewale George Adeniyi Department of Chemical Engineering, University of Ilorin, Ilorin, Nigeria Author

DOI:

https://doi.org/10.64229/8z9e3e95

Keywords:

Agriculture, Algorithm, ChatGPT, Sugarcane, Sustainability

Abstract

The sugar industry faces unprecedented challenges including climate variability, sustainability demands, and operational efficiency requirements, necessitating innovative technological solutions. While artificial intelligence (AI) applications are transforming various agricultural sectors, there is a lack of comprehensive analysis examining AI implementation across the entire sugar industry value chain from cultivation to supply chain management. This review aims to provide a comprehensive analysis of AI applications in the sugar industry, examining current implementations, economic and environmental impacts, regional variations in adoption, and identifying future research directions and implementation challenges. The study showed that AI is revolutionizing sugarcane and sugar beet cultivation through precision agriculture, optimizing resource management, and enabling early disease detection with high accuracy. In manufacturing, AI optimizes mill processes, enhances quality control, and facilitates predictive maintenance, leading to increased efficiency and reduced waste. Furthermore, AI improves supply chain management by enhancing demand forecasting and logistics. The adoption of AI yields substantial economic benefits, including increased production and reduced costs, while also promoting environmental sustainability through efficient resource utilization. Key challenges include data availability, infrastructure limitations, and the skills gap, but future trends point towards the integration of generative AI, advancements in robotics, and the development of smart farms and mills. In summary, AI offers significant potential to transform the sugar industry, driving efficiency, sustainability, and economic growth, but its successful implementation requires addressing key challenges and embracing future technological advancements.

References

[1]Okoro HK, Iwuozor KO, Isaac I, Egbemhenghe A, Ehiguina JI, Emenike EC, et al. Utilization of Sugarcane Bagasse-Derived Bioadsorbents in a Packed Bed System for the Treatment of Fish Pond Effluent. Sugar Tech, 2025, 27, 1171-1184. DOI: 10.1007/s12355-025-01575-5

[2]Kumar R. AI for Sugar Industry. Kindle Edition ed: Amazon; 2024. 1-200 p. ISBN-13: ‎979-8321936849.

[3]Lee JY, Naylor RL, Figueroa AJ, Gorelick SM. Water-food-energy challenges in India: political economy of the sugar industry. Environmental Research Letters, 2020, 15(8), 084020. DOI: 10.1088/1748-9326/ab9925

[4]Mohammed K, Iwuozor KO, Anyanwu VU, Olaniyi BO. Sugar dust explosion in the sugar industry: Case studies and prevention strategies. Sugar Tech, 2024, 26, 12-19. DOI: 10.1007/s12355-023-01307-7

[5]Mohamed MA, Hafez MM, Rabeiy R, El Nagreedy EA. Noise pollution: Assessing and control in the beet sugar industry. Egyptian Sugar Journal, 2020, 15, 39-51.

[6]Gyan I. International Sugar Organization 2024. Available at: https://www.iasgyan.in/daily-current-affairs/international-sugar-organization (accessed on 28 April 2025).

[7]Balakrishnan M, Batra V. Valorization of solid waste in sugar factories with possible applications in India: A review. Journal of Environmental Management, 2011, 92(11), 2886-2891. DOI: 10.1016/j.jenvman.2011.06.039

[8]GrandView. Sugar Market Size, Share & Trends Analysis Report By Product Type (White Sugar, Brown Sugar), By Form (Granulated Sugar, Powdered Sugar), By End-use, By Source, By Region, And Segment Forecasts, 2024-2030, 2024. Available at: https://www.grandviewresearch.com/industry-analysis/sugar-market-report (accessed on 28 April 2025).

[9]Stewart B. Transforming sugar manufacturing with the power of AI. Sugar Industry International, 2025, 150(2), 123-127. DOI: 10.36961/si33025

[10]Modi RU. Future prospects of artificial intelligence in sugarcane culture and in sugar industry. 2022. In: Recent Approaches for Doubling Farmers Income in Sugarcane Based Cropping System(pp.41-47)Chapter: 7Publisher: ICAR-Indian Institute of Sugarcane Research, Lucknow, UP, India.

[11]Hernandez-Palma HG, Alvarado JR, Guiliany JE, Rios AL. Technological tools based on artificial intelligence in the sugar industry: A bibliometric analysis and future perspectives for energy efficiency. Latin American Developments in Energy Engineering, 2023, 4(2), 49-64. DOI: 10.17981/ladee.04.02.2023.4

[12]Iwuozor KO, Olaniyi BO, Anyanwu VU, Suleiman MA, Omoleye WS, Enahoro-Ofagbe FE, et al. The effect of ChatGPT on sugar industry research. Sugar Tech, 2023, 25, 1278-1284. DOI: 10.1007/s12355-023-01300-0

[13]Ray PP. Generative ai and its impact on sugarcane industry: An insight into modern agricultural practices. Sugar Tech, 2024, 26(2), 325-332. DOI: 10.1007/s12355-023-01358-w

[14]Bhatt R, Hossain A, Majumder D, Chandra MS, Ghimire R, Faisal M, et al. Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings. Journal of Agriculture and Food Research, 2024, 18, 101519. DOI: 10.1016/j.jafr.2024.101519

[15]Indu PV, Nanjundan P, Thomas L. AI for optimization of farming resources and their management. AI in Agriculture for Sustainable and Economic Management: CRC Press; 2024, 42-52.

[16]Medhe PG. Use of artificial intelligence in sugar industry for proper and timely decisions to achieve sustainability, 2024. Available at: https://www.chinimandi.com/use-of-artificial-intelligence-in-sugar-industry-for-proper-and-timely-decisions-to-achieve-sustainability/ (accessed on 29 April 2025).

[17]Belghachi M. Smart irrigation systems using AI to optimize water usage. In Cases on AI-Driven Solutions to Environmental Challenges: IGI Global Scientific Publishing; 2025. p. 241-68. DOI: 10.4018/979-8-3693-7483-2.ch009

[18]Famonaut. Introduction to JEEVN AI: A Game-Changer for Florida’s Sugarcane Industry: Farmonaut Technologies Pvt. Ltd.; 2025. Available at: https://farmonaut.com/usa/revolutionizing-sugarcane-farming-in-florida-with-farmonauts-satellite-ai-based-farm-intelligence/ (accessed on 29 April 2025).

[19]Upadhye SA, Dhanvijay MR, Patil SM. Sugarcane disease detection using CNN-deep learning method: An Indian perspective. Universal Journal of Agricultural Research, 2023, 11(1), 80-97. DOI: 10.13189/ujar.2023.110108

[20]Kunduracıoğlu İ, Paçal İ. Deep learning-based disease detection in sugarcane leaves: Evaluating efficientnet models. Journal of Operations Intelligence, 2024, 2(1), 321-235. DOI: 10.31181/jopi21202423

[21]Gamaya. GAMAYA Granted Patent for AI driven Breakthrough Sugarcane Yield Prediction and Harvest Optimization Technology, 2024. Available at: https://www.gamaya.com/post/gamaya-granted-patent-for-ai-driven-breakthrough-sugarcane-yield-prediction-and-harvest-optimization (accessed on 29 April 2025).

[22]Vishwajeet S, Med RV, Subhash KY. Predictive modelling for sugarcane production: A comprehensive comparison of ARIMA and machine learning algorithms. Applied Biological Research, 2024, 26(2), 199-209. DOI: 10.48165/abr.2024.26.01.23

[23]Sridhara S, Soumya BR, Kashyap GR. Multistage sugarcane yield prediction using machine learning algorithms. Journal of Agrometeorology, 2024, 26(1), 37-44. DOI: 10.54386/jam.v26i1.2411

[24]Sun J, Sun C, Li Z, Qian Y, Li T. Prediction method of sugarcane important phenotype data based on multi-model and multi-task. PLOS ONE, 2024, 19(12), e0312444. DOI: 10.1371/journal.pone.0312444

[25]Zhu C, Wu C, Li Y, Hu S, Gong H. Spatial location of sugarcane node for binocular vision-based harvesting robots based on improved YOLOv4. Applied Sciences, 2022, 12(6), 3088. DOI: 10.3390/app12063088

[26]Angamuthu T. Hybrid deep learning technique for Identifying Diseases in Sugar-cane Crops. In Preprint. 2025. DOI: 10.21203/rs.3.rs-6145260/v1

[27]Medhe PG. AI can revolutionise sugarcane farming in India, boost yields, and cut costs: ChiniMandi, 2025. Available at: https://www.chinimandi.com/ai-can-revolutionise-sugarcane-farming-in-india-boost-yields-and-cut-costs/ (accessed on 29 April 2025).

[28]M.I.T. The 77 Lab: Pushing Human-Robot Interactions to Empower Humans, 2025. Available at: https://the77lab.mit.edu/agricultural-robotics/ (accessed on 29 April 2025).

[29]Alencastre-Miranda M, Davidson JR, Johnson RM, Waguespack H, Krebs HI. Robotics for sugarcane cultivation: Analysis of billet quality using computer vision. IEEE Robotics and Automation Letters, 2018, 3(4), 3828-3835. DOI: 10.1109/LRA.2018.2856999

[30]Farming F. Agrishow: crop protection 100% by robots and multifunctional truck for grains, 2024. Available at: https://www.futurefarming.com/tech-in-focus/agrishow-crop-protection-100-by-robots-and-multifunctional-truck-for-grains/ (accessed on 29 April 2025).

[31]Gurobi. Case studies: Optimizing business processes with customized AI solutions, 2025. Available at: https://www.gurobi.com/case_studies/c3-ai-optimizing-business-processes-with-customized-ai-solutions/ (accessed on 30 April 2025).

[32]Brett S. Optimize sugar production with AI: AI-enabled optimization improves operations and maximizes sugar mill profitability, 2024. Available at: https://c3.ai/wp-content/uploads/2024/07/Optimize-Sugar-Production-with-AI-new.pdf?utmMedium=fg8d04 (accessed on 30 April 2025).

[33]C3.ai. Sugar manufacturer increases yield with AI-driven setpoint recommendations, 2025. Available at: https://c3.ai/customers/sugar-manufacturer-increases-yield-with-ai-driven-setpoint-recommendations/ (accessed on 30 April 2025).

[34]AIT. New AI method for determining the quality of sugar beets, 2024. Available at: https://www.ait.ac.at/news-events/single-view?tx_ttnews%5Btt_news%5D=8199&cHash=15262c236376a357e795b6c28f22b2ba (accessed on 30 April 2025).

[35]ScanGrading. Cases: Automating processes for measuring and analyzing sugar crystal size distributions, 2025. Available at: https://www.scangrading.com/cases/ (accessed on 30 April 2025).

[36]Iwuozor KO, Ojeyemi T, Emenike EC, Umeh CT, Egbemhenghe A, Ayoku BD, et al. Management of sugar dust in the sugar industry. Heliyon, 2023, 10, e23158. DOI: 10.1016/j.heliyon.2023.e23158

[37]AI. AI-assisted sugar mill efficiency analysis, 2025. Available at: https://aimlprogramming.com/services/ai-assisted-sugar-mill-efficiency-analysis/ (accessed on 30 April 2025).

[38]Samuels A. Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: A systematic literature review. Frontiers in Artificial Intelligence, 2025, 7, 1477044. DOI: 10.3389/frai.2024.1477044

[39]AI. AI sugar factory automation, 2025. Available at: https://aimlprogramming.com/services/ai-sugar-factory-automation/ (accessed on 30 April 2025).

[40]eSided. Real-Time AI supply chain monitoring transforms sugar mill operations, 2025. Available at: https://esided.com/case-studies-ai-supply-chain-monitoring-sugar-mill-transformation/ (accessed on 30 April 2025).

[41]Farming F. Harvesting sugar cane more efficiently with AI and IoT, 2019. Available at: https://www.futurefarming.com/smart-farming/harvesting-sugar-cane-more-efficiently-with-ai-and-iot/ (accessed on 30 April 2025).

[42]ETV. Artificial intelligence proving to be a boon for sugarcane farmers in maharashtra, 2025. Available at: https://www.etvbharat.com/en/!state/artificial-intelligence-proving-to-be-a-boon-for-sugarcane-farmers-enn25041102394 (accessed on 30 April 2025).

[43]Yee CM. Chasing peak sugar: India’s sugar cane farmers use AI to predict weather, fight pests and optimize harvests, 2025. Available at: https://news.microsoft.com/source/asia/features/chasing-peak-sugar-indias-sugar-cane-farmers-use-ai-to-predict-weather-fight-pests-and-optimize-harvests/ (accessed on 30 April 2025).

[44]CropGPT. AI-powered sugarcane farming revolutionizes indias sugar industry, 2025. Available at: https://cropgpt.ai/ai-powered-sugarcane-farming-revolutionizes-indias-sugar-industry (accessed on 30 April 2025).

[45]Ahmed MO, Idris AM, Enahoro-Ofagbe FE, Iwuozor KO. Impact of irrigation management on sugarcane during stress. In Sugarcane Cultivation and Management: Apple Academic Press; 2024. p. 53-71. DOI: 10.1201/9781003504122-3

[46]Felipe B. Brazil Sugarcane farm uses AI detection to mitigate wildfire risks: Roboticscats, 2023. Available at: https://roboticscats.com/2023/07/11/brazil-sugarcane-farm-uses-ai-detection-to-mitigate-wildfire-risks/ (accessed on 30 April 2025).

[47]Phol M. Mitr phol pioneers AI to modernize thai sugar industry, 2019. Available at: https://sugar-asia.com/mitr-phol-pioneers-ai-to-modernize-thai-sugar-industry/ (accessed on 30 April 2025).

[48]Saengprachatanarug K, Chea C, Posom J, Saikaew K. A review on innovation of remote sensing technology based on Unmanned Aerial Vehicle for sugarcane production in tropical region. In Remote Sensing Application: Regional Perspectives in Agriculture and Forestry, 2022, 337-350. DOI: 10.1007/978-981-19-0213-0_12

Downloads

Published

2025-12-12

Issue

Vol. 2 No. 4 (2025)

Section

Articles

License

Copyright (c) 2025 Kingsley O. Iwuozor, Ejidike Lynda Chinyere, John C. Iwuozor, Ebuka Chizitere Emenike, Abel U. Egbemhenghe, Marycynthia Ebere Amadi, Adewale George Adeniyi (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.