This semester, our team worked on an image classification problem involving plants grown aquaponically. Our collaborators at Pure Reason AI are currently working on growing plants in a deep-water culture system here at LSU. This system works by transferring nutrients produced by fish to the plants that are grown using only water. This method takes away the need for soil and produces plants that only need water and nutrients to grow. Our goal this semester was to classify images of lettuce plants into one of four categories: fully nutritious (FN), potassium deficient (K), nitrogen deficient (N), or phosphorus deficient (P). We accomplished this goal by training two neural networks to detect the deficiencies in the images and classify them accordingly. This goal was met by researching similar projects, utilizing Python to create the neural networks, and training them to classify images on a dataset provided by our collaborators. Some future works for this project include researching more methods for image classification and developing our neural networks to handle issues such as light, shadows, and noise. Our collaborators are interested in classifying images based on light and possibly applying Spectral Theory to detect the deficiencies. We accomplished our goal of classifying images based on nutrient deficiencies, but more work is needed to help create a user-friendly system that can be used by anyone growing plants aquaponically.