AI-Powered Innovations in Agriculture Supply Chain Management

  • Authors

    • Amina Bello HR Manager, Dangote Group, Nigeria Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2020PII3N7P

    Published 07-05-2020

  • Artificial Intelligence, Agriculture Supply Chain, Machine Learning, Logistics Optimization, Smart Agriculture, Demand Forecasting, Food Security, Iot, Blockchain, Precision Agriculture

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    A. Bello, “AI-Powered Innovations in Agriculture Supply Chain Management”, IJETMR, vol. 3, no. 2, pp. 01–16, Jul. 2020, doi: 10.67228/30715636/IJETMR-2020PII3N7P.

  • Abstract

    Agricultural supply chains are complex systems involving multiple stakeholders such as farmers, logistics providers, processors, retailers, and consumers. These systems often face challenges including food loss, price fluctuations, poor demand–supply coordination, and low farmer profits. Artificial Intelligence (AI) offers innovative solutions to address these issues through predictive analytics, automation, and real-time monitoring. This paper examines AI-driven approaches for improving agricultural supply chain management using technologies such as machine learning, deep learning, Internet of Things (IoT), blockchain, and cloud computing. AI-based demand forecasting enhances market planning by analyzing historical data, weather patterns, and consumer trends, while smart logistics systems optimize routing and inventory management. Computer vision enables automated quality assessment of agricultural products, reducing post-harvest losses. The proposed multi-layer AI framework includes data acquisition, preprocessing, model training, optimization, and decision-support layers. Experimental evaluation demonstrates improvements in cost efficiency, delivery performance, waste reduction, and farmer income compared to traditional systems. The study also discusses challenges such as data privacy, infrastructure limitations, skill gaps, and implementation costs. Future research directions include autonomous logistics, edge computing, digital twins, and AI-driven policy frameworks for sustainable agriculture. Overall, AI-based supply chain management has the potential to significantly enhance efficiency, sustainability, and resilience in global agricultural systems

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