Molina, MartinMartinMolinaGodoy, JulioJulioGodoyCastro, John W.John W.CastroRiffo, VladimirVladimirRiffo2026-07-072026-07-072026COMPUTERS AND ELECTRONICS IN AGRICULTURE, 244, 111489 (2026). https://doi.org/10.1016/j.compag.2026.1114890168-16991872-7107https://hdl.handle.net/20.500.12740/24743In an era where global apple production exceeds 80 million tons annually, ensuring high fruit quality is essential for consumer satisfaction and economic success. However, surface defects like wounds, rot, and sunburn cause millions in losses through manual inspections, which are often subjective, inefficient, and costly in packing plants. This study fills important gaps in automated quality control by using advanced deep learning to classify apple damages with unmatched efficiency and industrial usefulness. Through a review of the literature and various web repositories that include information up to 2025, we constructed a novel, balanced dataset from scratch, capturing diverse real-world defects that were underrepresented in previous studies. We rigorously evaluated nine advanced convolutional neural network architectures -including VGG16/19, multiple ResNet variants, and YOLOv9c for classifying different types of damage in apples- before optimizing the topperforming ResNet101 through systematic hyperparameter tuning. Achieving an impressive 95% accuracy on unseen data for damage classification and 81% for preliminary detection, our optimized model aims to reduce waste and boost supply chain efficiency, setting a new standard for sustainable agriculture. Moving forward, this framework opens the door to multimodal integrations such as hyperspectral imaging and robotic sorting, adaptable to other fruits, transforming post-harvest processing and inspiring further innovations in AI-driven food security.AppleMachine learningDeep learningConvolutional neural networksClassificationArticulohttps://doi.org/10.1016/j.compag.2026.111489