Abstract
In today's rapidly evolving world, agricultural productivity has become a global challenge. Despite the rise of modern technologies, interest in agriculture has declined, primarily due to productivity issues caused by the spread of diseases and the difficulty farmers face in accurately diagnosing them. Traditionally, precise disease detection has relied on the expertise of seasoned professionals. To address this, the proposed application, Agriculture AI, empowers farmers by providing accurate disease predictions and actionable insights. The system leverages Ensemble Learning techniques in machine learning (ML) to predict plant diseases effectively. To enhance user trust and transparency, Explainable AI (XAI) is integrated to clarify predictions, making the system more reliable and user-friendly. All data, including predictions and supporting information, are stored in a centralized database. This database is a resource for researchers and developers to evaluate the application's accuracy, identify trends, and notify farmers of new or unidentified diseases. Additionally, the application facilitates communication between farmers, researchers, and developers, allowing users to raise queries and provide feedback. This comprehensive tool connects the gap between AI technology and agricultural needs, supporting global efforts to enhance productivity and sustainability in farming.