In-situ flexible non-destructive wireless sensing for bagged fruits

Although fruit bagging technology can enhance fruit quality and reduce damage, it poses an obstacle to the direct monitoring of fruit ripeness and quality, creating an urgent need to develop in-situ non-destructive in-bag sensing technologies. This study aims to design and develop an in-situ flexible wireless spectral sensing system (IFWS) based on flexible electronic technology to achieve in-situ, non-destructive, rapid monitoring and accurate sensing of bagged fruit quality. The IFWS fabricates flexible circuit by laser etching Cu/PI film, integrating visible spectral sensor, microcontroller, and other electronic components, which endows the system with excellent flexibility, overcoming the space constraints and surface-fitting challenges faced by rigid sensors in bagged fruit quality inspection. Based on spectral data collected by the IFWS, the MLR model was used to analyze the correlation between the spectral data and bagged fruit quality parameters (SSC, TA, color parameters L*, a*, and b*). The developed model exhibits robust predictive performance, enabling accurate prediction of sugar accumulation (R²cv = 0.865), acid metabolism (R²cv = 0.635), and pigment synthesis (R²cv>0.86). Results from prediction set validation showed that the RPD for SSC prediction was 2.7, with RPD values for color parameters all exceeding 3, confirming the applicability of the IFWS in predicting bagged fruit quality. When deployed in the growing environment of bagged fruits, the IFWS successfully captured dynamic changes in quality during fruit growth from two dimensions (temporal evolution and population variation), validating its predictive efficacy in complex field scenarios. By integrating the MLR model algorithm into the microcontroller, real-time visualization of quality predictions was achieved, further enabling rapid assessment of quality and ripeness of bagged fruits. In summary, the IFWS integrates flexible electronic technology with machine learning algorithms to realize digital quality assessment of bagged fruits, thereby providing critical technical support for the development of precision and digitalization in smart agriculture and holding significant application value for promoting efficient management of bagged fruits.