Before revision:  First, we designed a deep convolutional neural network (CNN) that took advantage of state-of-the-art modeling and solution techniques to extract stress metrics for each type of stress. Our CNN model was found to successfully distinguish between the low and high stress environments due to considering multiple factors such as planting/harvest dates, daily weather, and soil conditions. Then, we conducted a linear regression of the yield of hybrid against each stress metric, and classified the hybrid based on the slope of the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress .
After revision:  In this paper, we present an unsupervised approach to solving this problem, which was recognized as one of the winners in the 2019 Syngenta Crop Challenge .