This paper presents presents an automated apple classification system using near-infrared (NIR) imaging and convolutional neural networks (CNNs) to distinguish between apples in good condition, beaten, and rotten. The methodology relies on processing NIR images to capture subtle differences in the texture and composition of apple surfaces, which enables precise classification through CNNs. The CNN algorithms optimize the identification of characteristic patterns associated with each category, and the results demonstrate the effectiveness of this approach compared to other state-of-the-art methods.