India's agricultural sector is a vital pillar of its economy, providing livelihoods to millions of people. However, plant diseases position a major threat to crop productivity, leading to significant financial losses. The unpredictable nature of climate change has further exacerbated the spread of these diseases, highlighting the need for early and accurate detection to prevent large-scale crop damage. Traditional disease identification methods, which rely on human observation, are often ineffective, subjective, and prone to misdiagnosis. Incorrect assessments may result in the misuse of pesticides, causing economic burdens and environmental harm. Consequently, the development of an advanced and reliable plant disease detection system is essential for promoting sustainable farming practices. The rise of artificial intelligence and image processing has introduced innovative techniques for detecting plant diseases. Deep convolutional neural networks (CNNs) have demonstrated exceptional efficiency in identifying and classifying plant diseases with high accuracy. These models utilize sophisticated machine learning techniques to analyze high-resolution leaf images, ensuring fast and precise disease detection. This study aims to design an advanced CNN- based model to improve the accuracy and effectiveness of plant disease identification, providing farmers with actionable insights for better disease control. By harnessing AI-driven technologies, this research seeks to reduce agricultural losses, enhance crop yields, and contribute to the long-term sustainability of India’s agricultural sector. Additionally, integrating such intelligent systems can optimize resource management and reduce reliance on harmful chemical treatments.