An acoustic-mechanical sensing system with multimodal machine learning techniques for in-line quality grading of watermelons

Abstract

A complete assessment of both internal and external quality parameters of watermelons is essential for export. However, small and medium-sized watermelon export enterprises often face challenges in accessing cost-effective and integrated grading systems. This study proposes an acoustic-mechanical sensing system for classifying watermelons based on both sweetness and weight. By combining weight measurement and sweetness estimation through acoustic analysis at a single station, the proposed system achieves a compact design and reduces data acquisition time. Additionally, a multimodal machine learning approach is applied to classify watermelon quality accurately. Among the tested models, the K-Nearest Neighbors model achieves the highest classification performance, with an accuracy of 97.3% and a precision of 96.6%. With its strong classification ability, integrated design, and low cost, the proposed system shows great potential for automated in-line quality grading of watermelons and other agricultural products. Unlike conventional large-scale systems that cascade individual grading functions, the integrated and cost-effective design of this system is suitable for small and medium-sized watermelon export enterprises to apply at each distributed shipping facility during intensive periods.