mmFruit: A Contactless and Non-Destructive Approach for Fine-Grained Fruit Moisture Sensing Using Millimeter-Wave Technology

• Published in: IEEE transactions on mobile computing 2024-12, p.1-18
• Main Authors: Niaz, Fahim, Zhang, Jian, Khalid, Muhammad, Younas, Muhammad, Niaz, Ashfaq
• Format: Magazinearticle
• Language: English
• Subjects: Accuracy; Attenuation; Contact-less Sensing; Estimation; Millimeter Wave; Millimeter wave communication; Mobile computing; Moisture; Moisture measurement; Moisture Sensing; Monitoring; Reflection; Sensors; Wireless Sensing
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2024.3520914

Wireless sensing offers a promising approach for non-destructive and contactless identification of the moisture content in fruits. Traditional methods assess fruit quality based on external features such as color, shape, size, and texture. However, fruits often appear perfect externally while being rotten inside. Thus, accurately measuring internal conditions is crucial. This paper introduces mmFruit, a non-destructive and ubiquitous system that employs mmWave signals for precise and robust moisture level sensing in thin and thick pericarp fruits. We propose a novel dual incidence moisture estimation model for regular moisture monitoring to achieve high granularity and eliminate fruit type and size dependency. Additionally, we leverage unique reflection responses across different mmWave frequencies to provide discriminative information about fruit moisture levels. Our comprehensive theoretical model demonstrates how fruits' refractive index, attenuation factor, and elasticity can be estimated by eliminating fruit type dependency. We developed an electric field distribution model utilizing two receiving antennas to address the challenge of varying fruit sizes through a differential approach, aiming to improve overall robustness. mmFruit integrates a customized Spatial-invariant network (SpI-Net) to eliminate interference from different frequencies and locations, ensuring stable moisture monitoring regardless of target displacement. Extensive experiments were conducted over a month in varied environments on seven types of fruits with thin and thick pericarps (apple, pear, peach, mango, orange, dragon fruit, and watermelon). The results demonstrate that mmFruit achieves a commendable RMSE of 0.276 in moisture estimation. It accurately distinguishes fruits with minor moisture level differences (0% to 7%) with 93.6% accuracy and higher moisture differences (45% to 65%) with over 95.1% accuracy, even in scenarios involving diverse displacements and rotations.