Precision Screening in Plant Pathogen Management using a Machine Learning Approach

Abstract

This research explores the integration of sensors and microfluidics in the realm of precision drug screening for effective plant pathogen management. Leveraging a machine learning approach, we propose a comprehensive system that combines advanced sensing technologies, microfluidic platforms, and artificial intelligence for precise and timely detection of plant pathogens. The study focuses on optimizing drug screening processes, enhancing the efficiency of pathogen identification, and improving overall crop health management. The synergy between microfluidics, sensors, and machine learning not only streamlines the drug screening pipeline but also enables real-time decision-making for proactive pathogen control in agriculture.