Explainable AI and mobile imaging for non-destructive avocado ripeness and internal quality assessment to reduce food waste

Abstract

Food waste is a global challenge, primarily driven by inaccurate prediction of food quality and remaining shelf life. Approximately 30% of food waste occurs at retail and household levels, where traditional assessment methods such as subjective visual inspection or destructive instrumental analysis are inefficient. In this study, we integrated smartphone imaging and deep learning to non-destructively predict avocado firmness and internal quality, aiming to support smarter consumption and distribution decisions. Avocados were selected as a representative food model due to their high market value and high waste rate (∼40%). Over an eight-day storage period at room temperature, a dataset of 1,400 avocado images was collected using a smartphone. Firmness was measured using a texture analyzer and served as the ripeness metric and ground truth for model training. To predict ripeness, a convolutional neural network residual regression (CNN ResNetR) model achieved the highest accuracy (R 2 = 0.92), outperforming support vector machine regression (R 2 = 0.82) and random forest (R 2 = 0.86) models. Predicted firmness values were further mapped to recommend remaining shelf life using industrial guidelines. To assess internal quality, state-of-the-art CNN and vision transformer models were developed to classify avocados as fresh or rotten, achieving an accuracy above 84%. Model interpretability was obtained using the Local Interpretable Model-Agnostic Explanations (LIME) technique, which identified key image features influencing classification. This deep learning-enabled framework offers a rapid, scalable, and non-destructive solution to evaluate avocado ripeness and internal quality, with the potential to reduce food waste and improve decision-making across the supply chain.

Topics & Concepts