A Sustainable AI-Enabled Framework for Automated Crop Disease Diagnosis and Agricultural Advisory

Abstract

Diseases affecting crops threaten farmers incomes, food security, and agricultural production. Traditional methods for detecting diseases rely on consultation with professionals, as well as inspections made by a person, and are often quite labor-intensive. In this paper, the system that uses artificial intelligence (AI) algorithms to give treatment recommendations for crops based on their geographical location, and using techniques like deep learning to detect automatically the plant diseases from photos. The system has four key modules: image acquisition and preprocessing, disease classification, and generation of treatment recommendations. The experimental results show improvement in the ability to manage crop health, the ability to implement modern precision agriculture, and increased accuracy in diagnosing diseases more rapidly and providing better decision support to farmers.