Non‐destructive grading technique for mangoes using a flexible impedance sensing system and YOLOv5s_CBAM

Abstract

AbstractFlexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.Practical applicationsThe purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.