Agriculture, contributing approximately 18% to India’s GDP, faces significant challenges due to biotic stresses, with up to 33% of annual crop losses attributed to diseases and insect-pests infestations. Traditional manual diagnosis by the domain experts is limited by time, cost, and accessibility, necessitating automated solutions for sustainable agriculture. AI-DISC (Artificial Intelligence-based Disease Identification System for Crops), an Android-based mobile application developed by ICAR-IASRI, addresses this challenge, aiming to mitigate crop losses. Utilizing the National Image Base for Plant Protection (NIBPP) database with approximately 4.97 lakh high-quality images of diseases and insect-pests of various crops, AI-DISC employs advanced AI-driven deep learning models, including convolutional neural networks, YOLOv5, and Transformer-based architectures, to achieve high-accuracy identification of diseases and insect-pests across 26 agriculturally important crops. These AI-based models, trained on curated and pre-processed NIBPP images, were deployed on the Krishimegh cloud infrastructure at ICAR-IASRI, integrated with the AI-DISC app via FLASK-based web APIs. The app’s Disease and Pest Identification Module (DPIM) enables rapid, field-based diagnosis through smartphone-captured images, providing farmers with real-time advisories and management strategies within seconds. AI-DISC app emphasizes reduced reliance on labor-intensive inspections, enhancement in food security, and promoting sustainable farming practices in India.