Lettuce is a crucial component of a healthy, well-rounded diet. The annual consumption of leafy vegetables in the U.S. is approximately 6 kg per capita. While most lettuce crops are traditionally grown in open fields, there has been a recent increase in production within controlled environment systems. U.S. lettuce growers are currently facing several challenges, including labor shortages and rising costs, water scarcity, high fertilizer costs, and concerns about food safety. Lettuce production and quality control processes are labor-intensive. Traditionally, the nutrient composition of lettuce has been determined through laboratory tests conducted by trained technicians. However, advancements in technology, such as computer vision and digital imaging, offer the possibility of obtaining real-time data while reducing labor costs. This study utilized hyperspectral image data and artificial neural networks (ANN) to estimate the nutrient composition and quality of lettuce grown in a controlled environment. One challenge in the application of ANN and other machine learning algorithms is selecting the most appropriate features to prevent overfitting in predictive model development. To address this, various feature selection and data size reduction methods were explored. The results of the study indicate that the ANN model accurately classified lettuce contents at a rate of 100% and estimated nutrient composition within the range of 0.85 to 0.99. Different feature selection approaches from the hyperspectral image data were compared, including first-order derivatives, principal component analysis, partial least squares regression, multivariate regression, and variable importance projection.