Drought stress is a major issue in agriculture, as it is becoming more prevalent due to climate change and can reduce crop yields significantly. Low water amounts, particularly at the flowering stage, limit plants from undergoing typical growth and reproductive development, including gametogenesis, fertilization, and embryogenesis. Therefore, it is important that rice growers have a method of quickly determining whether or not their crops are undergoing drought stress to enact mitigation strategies. This study aims to use machine learning computer vision to accurately detect drought stress in plants. Before I developed models, I conducted data pre-processing on images of Setaria plants with varying water contents. Then, I built several machine learning models, including K-Nearest-Neighbors, Decision Tree, Logistic Regression, and ResNet 18. I then assessed the effectiveness of these models by calculating their accuracy scores. I found that K-Nearest-Neighbors, Decision Tree, and Logistic Regression models obtained 80.2%, 70.7%, and 75.9% accuracy scores, respectively, while ResNet18 obtained a very high accuracy of 97.3%. Therefore, these models can serve as a promising future solution for non-invasively detecting drought stress in rice plants, potentially saving crop producers millions of dollars in yield.