MACHINE LEARNING APPROACHES FOR IDENTIFYING, TRACKING, AND PREDICTING PLANT DISEASES IN AGRICULTURE

Abstract

Plant diseases pose significant challenges to agricultural productivity, threatening crop yield and food security. Timely identification, 
tracking, and prediction of plant diseases are essential for minimizing the impact on crops and optimizing farming practices.Traditional methods of disease detection are often labor intensive and time-consuming, making them inefficient for large-scale agricultural 
operations. In recent years, machine learning (ML) has emerged as a powerful tool to automate and enhance plant disease 
management by improving the accuracy and efficiency of disease identification, monitoring, and forecasting.