Abstract

This research paper investigates the utilization of machine learning methods in the domain of banana disease detection, with the objective of improving the accuracy and effectiveness of disease diagnosis. Many diseases pose serious risks to banana crops, which are essential for maintaining global food security and can result in large yield losses. This study offers a comprehensive analysis of the situation of both conventional and contemporary methods for detecting banana disease. The study focuses on current developments that have improved the precision and effectiveness of disease diagnosis, including the use of cutting-edge technology like machine learning and remote sensing. This research could completely change the way banana diseases are managed by giving farmers a reliable, accurate, and timely tool for early disease detection. For banana growers, this can result in higher-quality produce, higher yields, and more profitability. The created system can also be modified to identify illnesses in different crops, which will greatly enhance agricultural output and food security overall.