Abstract

The increasing frequency and severity of plant diseases pose significant threats to global food security and agricultural sustainability. This research paper addresses the urgent need for an integrated framework that combines machine learning algorithms and preventive measures to mitigate the impact of plant diseases. The study explores the development of a predictive model to forecast potential outbreaks. Its machine learning algorithms analyze datasets, learning patterns that are often imperceptible to the human eye enabling it to diagnose multiple diseases affecting various plants. The paper also investigates the implementation of preventive measures. The paper offers a comprehensive framework for early detection and proactive management of plant diseases. Ultimately, this research seeks to pave the way for a more resilient and efficient agricultural ecosystem in the face of evolving challenges posed by plant diseases.