A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness

Abstract

The integration of a non-invasive microwave imaging system with a machine learning algorithm could improve food quality and food safety. In this paper, a S- and C-band microwave imaging system that utilizes DAS (Delay and Sum) beamforming with an automated high-frequency switching network is built to scan watermelons and determine their ripeness. A total of 288 images were collected from eight different watermelons varying the height and angle of capture. A convolutional neural network (CNN) was employed to assess the ripeness level, which was determined by analyzing the Brix sugar content. The results show 86% accuracy for ripeness classification in three fold cross validation. This novel approach demonstrates the potential of combining microwave imaging with machine learning for non-destructive food quality assessment, offering a scalable and reliable tool for real-time evaluation of fruit ripeness and quality.