AI and blockchain in agribusiness: Transformative applications in supply chain management

Introduction

Agribusiness supply chain management represents one of the most important infrastructure systems for global food security, economic development, and poverty alleviation. The global agricultural supply chain manages the production, processing, distribution, and retail of diverse food products while navigating complex challenges of biological variability, environmental constraints, and market dynamics. According to data from the Central Institute for Post-Harvest Engineering and Technology (ICAR CIPHET 2015), India experiences approximately ₹ 92,651 crores in annual economic losses due to post-harvest inefficiencies across cereals, fruits, vegetables, spices, and livestock products. The agribusiness supply chain possesses unique characteristics distinguishing it from conventional manufacturing and retail supply chains. These include pronounced seasonality with cyclical production and demand patterns, inherent perishability requiring strict time management and controlled environments, quality variability across different harvests and cultivators, and geographic dispersion across vast production and distribution networks. These factors create substantial operational complexity requiring sophisticated management approaches. Supply chain management has undergone significant transformation over multiple industrial revolutions. Supply Chain prevalent in the pre-1990s era, relied on manual processes and limited technology, characterized by inefficiencies and lack of real-time data visibility. Subsequently technologies were introduced like the Enterprise Resource Planning (ERP) systems and enhanced data processing capabilities, enabling global reach but facing integration challenges and data silos. In the 2000's supply chain embraced digitalization through advanced software, improved connectivity, and GPS tracking, though cybersecurity concerns and complex network management emerged as significant challenges. The current paradigm integrates Internet of Things (IoT), Artificial Intelligence, big data analytics, and automation technologies. This evolution enables predictive analytics, enhanced operational efficiency, and data-driven decision-making across entire supply networks. The convergence of these technologies provides unprecedented opportunities for agribusiness transformation. Artificial intelligence (AI) is a branch of computer science that creates systems and software capable of performing tasks that typically require human intelligence, such as reasoning, learning, perception, and language understanding. These systems analyse data, recognize patterns, and make decisions with unprecedented speed and accuracy" (ISO, 2024). Blockchain is a decentralized, distributed digital ledger that records transactions across multiple computers, ensuring that the information is secure, transparent, and immutable. Each block contains a set of transactions, and once a block is completed, it is added to the chain in chronological order, making tampering extremely difficult without the consensus of the network.

Problem Statement and Objectives

AI Applications in Agribusiness Supply Chain Management

Contemporary AI applications across agribusiness supply chains address specific operational challenges: ML models analyse historical demand patterns, seasonal variations, and external variables to predict future requirements, enabling optimal inventory levels minimizing both stockouts and excess inventory. Computer vision systems analyse product characteristics (shape, size, colour, surface defects) with accuracy and consistency surpassing human inspectors, enabling rapid, large-scale quality assessment. AI algorithms optimize transportation routes considering traffic, distance, delivery windows, and vehicle capacity, reducing delivery times, fuel consumption, and costs. Data analytics and sensor systems optimize farming practices including irrigation timing, fertilizer application, pest management, and harvest scheduling based on real-time field conditions. Blockchain systems create immutable records of product movement from farm through consumer, enabling certification verification, contamination source identification, and consumer trust. IoT sensors and ML models predict equipment failures before occurrence, enabling preventive maintenance reducing downtime and operational disruptions. Recent reviews emphasize AI's expansion into decision-making across the value chain, including IoT for real-time farm monitoring and blockchain for traceability, enabling predictive risk management and financial planning for smallholders (Shravanthi et al., 2025) [25] .

Research Methodology Methodology Framework

This review adopts a literature review combined with realworld case study analysis. The methodology integrates: Review of peer-reviewed articles, industry reports, and academic research on AI applications in supply chain management, published primarily from 2020 onwards to capture contemporary developments. In-depth examination of six documented implementations of AI in agribusiness supply chains, selected for diversity of applications and geographic representation.

Solution Implementation

Infosys, in collaboration with agricultural exporters, implemented blockchain-based traceability system for mango export supply chain connecting India to Germany.

The system involved: Stakeholders in Distributed Network:  Farmer (Raj Kishor from Uttar Pradesh)

 Processor/Exporter (Mohan -Royal Pulp Factory owner)  International Importer (Charles Becker -Becker Packaged Foods Germany)  Certification Agencies (verifying quality and compliance)  Logistics Companies (managing transportation)  Banks (facilitating international payments)  Customs and Excise authorities

Real-World Context: EU Ban on Alphonso Mangoes

This case study reflects real challenges in agricultural trade.

In 2014, the European Union banned imports of Alphonso mangoes and select vegetables from India due to biosecurity concerns regarding pest presence and inadequate certification verification. Blockchain traceability systems address these concerns by providing irrefutable records of quality verification and pest management compliance throughout supply chains. Blockchain traceability extends across agricultural products including coffee, tea, spices, organic produce, and specialty crops where certification compliance and consumer transparency provide competitive advantages. The technology proves particularly valuable for Export-Oriented Products requiring international certifications (organic, Fair Trade, GI tags), High-value crops commanding price premiums justified by documented quality and certification, Products targeting consumers valuing sustainability and ethical sourcing information, Complex supply chains involving multiple processing and distribution stages.

Case

Acknowledgement

AcknowledgementsThe author expresses sincere gratitude to mentors Dr. Yashpal Singh, Dr. Bablu Kumar, and Dr. Debopam Rakshit for their invaluable guidance throughout this research. Dr. Yashpal Singh provided valuable insights to improve the study and incorporate the case studies. Dr. Bablu Kumar for his mentorship.

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