Goss Wilt has become a common disease in corn fields in North Dakota.  It has been one of the most yield-limiting diseases, causing losses of up to 50%. The current method to identify the disease is through visual inspection of the field, which is inefficient, and can be subjective, with misleading results, due to evaluator fatigue. Therefore, developing a reliable, accurate, and automated tool for assessing the severity of Goss's Wilt disease has become a top priority. The use of unmanned aerial vehicles (UAVs) in agricultural applications is increasing because of their capacity to gather high-quality data quickly and substitute human labor. This study implemented machine learning (ML) algorithms to assess the severity of Goss's Wilt disease in a corn field. Image data was collected by flying an UAV over a corn field in Horace, ND. After the initial image stitching process, a total of 270 plot images were obtained. An augmentation dataset containing 1326 images was prepared from the plot images using augmentation techniques including rotation and flipping. From each plot image, two different types of features were extracted: textural (contrast, dissimilarity, homogeneity, angular second moment) and color (hue, saturation, value, brightness, chromatic components: a* and b*, red, green, blue). A total of six different ML algorithms, including Logistic Regression, Ada Boost, Gradient Boosting, Support Vector Machine, Multilayer Perceptron, Random Forest, Naive Bayes, and K-Nearest Neighbors, were implemented for assessing the disease severity. Eighty percent of the dataset was used for training algorithms, and the remaining 20% was used for evaluation. Models were evaluated using precision, recall, and F-score. In this study, random forest achieved precision, recall, and F-score of 0.81 and outperformed other classifiers. The Naive Bayes yielded comparatively lower precision (0.51), recall (0.51), and F-score (0.52). Therefore, the Random Forest algorithm coupled with UAV imagery can be a potential tool for Goss's Wilt disease severity assessment in corn.