Quantitative Analysis of Fruit Internal Physical and Chemical Indicators based on Magnetic Induction Tomography

Non-destructive measurement of physical and chemical indicators (PCIs) in fruits and vegetables is essential for quality control in agriculture. However, existing techniques like hyperspectral and near-infrared spectroscopy face limitations in terms of high costs, noise sensitivity, low efficiency, and reduced accuracy under real-world conditions. In this work, we propose a novel approach using Magnetic Induction Tomography (MIT) to address these issues, offering enhanced accuracy, noise resistance, and cost-effectiveness. Specifically, we design and implement a PCIs measurement system based on MIT, which is previously unexplored in this context. In addition, we develop an efficient, portable system with customized regression models that map MIT conductivity data to quantitative PCI values, enabling practical field applications. Both controlled and real-world experiments show that our MIT system achieves an accuracy of 97% and 81% in predicting the freshness of tomatoes and grapes, respectively, and improves the R 2 value in tomato acidity prediction by 32.9% over near-infrared methods, demonstrating its effectiveness for non-destructive agricultural quality assessments.