Handling complex backgrounds and light perturbations for enhancing learning tasks from images of vegetables

Abstract The quality assessment of fruits and vegetables is crucial in the agroalimentary supply chain, as it directly affects consumer satisfaction, market value and overall food security. Traditional approaches rely on visual inspections or destructive techniques, which are labor-intensive and time-consuming. On the contrary, non-destructive techniques emerged as promising alternatives, offering solutions that can be adopted in real environments. Previous studies emphasized that the color distribution over images plays a significant role in the quality evaluation of food. In this paper, we propose a solution that leverages an autoencoder architecture to extract groups of relevant colors from the complete histogram of colors. To enhance the analysis of real-world images with complex backgrounds, we employ a pre-trained U2-Net architecture for background removal. Moreover, we propose a novel procedure based on outlier detection to identify and remove parts of the background that are not fully eliminated, especially along the edges of the product. After this preprocessing, we extract a complete color histogram which is fed to an autoencoder architecture, to extract high-level features representing color groups at different levels of granularity. The goal is to make the learned models less sensitive to light and color perturbations. Our experiments, conducted on two real-world datasets related to two different learning tasks, demonstrated the effectiveness of the proposed solution, that outperformed several baseline and state-of-the-art approaches, also based on complex neural network architectures.