Chapter One — 1.1 Background to the Study

Explainable AI has become one of the more actively explored innovations in the design of modern agricultural supply chain management, promising gains in efficiency and reliability that legacy, largely manual approaches have struggled to deliver.

In practice, however, adoption of explainable AI within agricultural supply chain management has been uneven, and its actual impact on real-time monitoring capability is not yet well understood in a rigorous, evaluable way — a gap this study is positioned to address.

1.2 Statement of the Problem

Current agricultural supply chain management in many organizations struggle with inadequate real-time monitoring capability, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating explainable AI, these limitations are likely to persist, exposing organizations to inefficiency, risk, and a poor user experience. This study is motivated by the need to design and evaluate a explainable AI-based approach to addressing this problem.

1.3 Objectives of the Study

1. To design and implement a explainable AI-based approach to improving real-time monitoring capability in agricultural supply chain management.
2. To evaluate the effectiveness of Explainable AI in enhancing real-time monitoring capability within agricultural supply chain management.
3. To identify the key requirements and constraints relevant to deploying explainable AI in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can explainable AI be applied to improve real-time monitoring capability in agricultural supply chain management?
2. How effective is Explainable AI at enhancing real-time monitoring capability within agricultural supply chain management?
3. What requirements and constraints are relevant to deploying explainable AI in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

This study is significant to software developers and system architects seeking practical guidance on applying Explainable AI within agricultural supply chain management. It is equally relevant to organizations that rely on these systems, offering a reference point for evaluating whether such an investment is justified, and it adds to the growing body of work on explainable AI applications in software technology / IT.

1.6 Scope of the Study

As a study of this kind, its scope is confined to designing and evaluating a explainable AI-based solution for agricultural supply chain management, focused specifically on real-time monitoring capability; broader deployment considerations fall outside this scope.