SGI-JOURNAL OF SCIENCE AND TECHNOLOGY (SGI-JST)

REVOLUTIONISING THE FERTILISER INDUSTRY THROUGH STRENGTH-FOCUSED AI INTEGRATION

E-ISSN: 9663-3691

P-ISSN: 4493-4712

DOI: https://iigdpublishers.com/article/1275

This article examines the transformative role of Artificial Intelligence (AI) in revolutionising the fertiliser sector by integrating cutting- edge technologies in procurement, manufacturing, logistics, and precision farming. Based on the principle of Strength- Focused AI Integration, it highlights harmonising AI’s data-driven strengths with human strategic judgment and ethical oversight, inspired by Vedic values that emphasise alignment with natural talents. The paper discusses AI tools-such as predictive analytics for sourcing raw materials, process optimisation for energy- heavy chemical reactions, computer vision for quality checks, and machine learning for tailored fertiliser advice-that boost efficiency, sustainability, and profitability throughout the supply chain. It reports measurable gains including higher yields, lower emissions, predictive maintenance extending asset life, and more resilient supply chains. Implementation challenges in regions like India and the Middle East are addressed with strategies emphasising local data, digital infrastructure, and workforce development. The framework regards AI not as a decision- maker replacement but as an' information generator' that enhances human expertise and supports proactive planning and innovation. It also emphasises AI' s critical role in environmental sustainability through reducing greenhouse gases, promoting circular economy practices, and managing risks. By integrating ethical, explainable AI into industry workflows, this approach fosters a sustainable model balancing productivity with social and environmental responsibility. The article presents a pathway for fertiliser companies to transition from traditional to digitally advanced, eco- conscious operations, illustrating how AI can drive economic competitiveness and ESG objectives. Combining technological accuracy with human contextual understanding, it redefines fertiliser production and distribution as key to global food security and sustainability. This comprehensive vision shows that AI- human collaboration, guided by philosophical insights and contextual awareness, can tackle industry volatility, resource scarcity, and climate challenges, paving the way for a smarter, more sustainable agriculture future. 

Keyword(s) AI-driven Fertiliser Optimisation, Precision Agriculture, Supply Chain Resilience, Sustainable Production, Strength-focused AI Integration.
About the Journal VOLUME: 10, ISSUE: 1 | February 2026
Quality GOOD

Partha Majumdar

Aashu, Rajwar, K., Pant, M., & Deep, K. (2024, May 23). Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues. Retrieved September 10, 2025, from https://arxiv.org/html/2405.17465v1 


Abdalla, O., Tajuddin, H. A., & Jami, M. S. (2023, December 31). AI-based waste management optimization in the halal food industry of Malaysia: A mini review. Retrieved September 10, 2025, from https://journals.iium.edu.my/bnrej/index.php/bnrej/article/vie w/89 


Amoo, O. O., Sodiya, E. O., Umoga, U. J., & Atadoga, A. (2024, February 28). AI-driven warehouse automation: A comprehensive review of systems. Retrieved September 10, 2025, from https://www.researchgate.net/publication/378307805_AIdriven_warehouse_automation_A_comprehensive_review_of_systems 


Anderson, K. (2024, December 18). The environmental challenges surrounding fertilisers. Retrieved September 10, 2025, from https://greenly.earth/engb/blog/industries/the-environmental- challenges-surrounding-fertilizers 


Anwar, H., Anwar, T., & Mahmood, G. (2023, December 25). Nourishing the Future: AI-Driven Optimization of Farm-to- Consumer Food Supply Chain for Enhanced Business Performance History. Retrieved September 10, 2025, from https://www.researchgate.net/publication/377780807_Nourishing_the_Future_AIDriven_Optimization_of_Farm-toConsumer_Food_Supply_Chain_for_Enhanced_Business_Performance_History 

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