Detection of Diseases in Tomato Plant Using CNN Model

This paper introduces a CNN-based system tailored for detecting diseases in tomato plants, aiming to deliver precise diagnosis, reduce crop damage, and support sustainable agriculture. The agricultural sector plays a crucial role in ensuring food security and sustaining the global economy. However, the increasing prevalence of crop diseases poses significant threats to crop yield, quality, and farmer livelihoods. Traditional disease detection depends heavily on expert visual inspections, which are slow, inefficient, and often inaccurate, especially in large-scale agricultural setups. Real-time image analysis powered by machine learning presents a scalable and reliable solution to identify plant diseases early. By leveraging high-resolution images and analyzing these with sophisticated algorithms, farmers can receive timely alerts and actionable insights to mitigate disease spread.