Machine learning can be used as a modern tool for plant disease diagnosis by developing algorithms that can accurately identify diseases from images. The document outlines the importance of early and accurate plant disease diagnosis. It then discusses traditional diagnosis methods and highlights machine learning and deep learning as current promising approaches. Key steps in machine learning algorithms include data collection, preprocessing, feature extraction, and model training and prediction. Applications discussed include disease identification, crop monitoring, greenhouse climate control, and linking models to decision support systems for farmers.