Few shot learning for avocado maturity determination from microwave images

Abstract

Artificial intelligence (AI) has played a critical role in the enhancement and automation of global food production and delivery. The assessment of ripeness through microwave images and AI presents a unique opportunity to enhance the current process and minimize food wastage. Particularly in avocado maturity prediction, microwave imaging provides a non-invasive solution to see through the shell and predict the maturity intelligently. However, conventional deep learning models are generally data-hungry, requiring huge amounts of training data to maintain high performance. In this paper, we propose a few-shot learning (FSL) model to address the need for a large training dataset in maturity estimation. We classify avocado microwave images for maturity assessment through a prototypical FSL model. The FSL model achieves an accuracy range of 80%–96 % across the different experimental groups and all outperforms the conventional deep learning in scenarios where limited data is available for training. This experiment demonstrates the feasibility and accuracy of utilizing microwave scanning and FSL to determine avocado maturity.