Comparative Analysis of Machine Learning Algorithms for Plant Leaf Disease Detection

Agriculture remains a cornerstone of economic stability and food security for many nations around the world. However, plant diseases caused by various pathogens such as fungi, bacteria, and viruses continue to threaten crop productivity, leading to severe financial losses and food supply issues. Early and precise detection of these diseases is critical for implementing timely intervention measures and preserving both yield quality and quantity. This study focuses on leveraging machine learning techniques to automate the classification of plant leaf images into healthy and diseased categories. Since symptoms of infection are most commonly observed on the leaves, the research emphasizes the analysis of leaf imagery. A comprehensive, preprocessed dataset comprising images of leaves from multiple plant species, affected by various diseases, was utilized for training and evaluation purposes. Multiple machine learning algorithms, including Random Forest, Naive Bayes, and XGBoost, were applied and assessed using performance metrics such as accuracy and confusion matrices. The primary goal was to identify the model that offers the most reliable and scalable solution for real-time plant disease detection. Among the models tested, XGBoost exhibited the highest classification accuracy, reinforcing its potential in agricultural monitoring systems.This research underlines the importance of technological advancements, particularly in machine learning, in transforming traditional farming practices. It paves the way for the development of intelligent, automated plant health monitoring tools that could assist farmers in mitigating crop losses and enhancing sustainable agricultural practices