Flexible Vis/NIR wireless sensing system for banana monitoring

Abstract

Abstract Objectives The quality of the fruit seriously affects the economic value of the fruit. Fruit quality is related to many ripening parameters, such as soluble solid content (SSC), pH, and firmness (FM), and is a complex process. Traditional methods are inefficient, do not guarantee quality, and do not adapt to the current rhythm of the fruit market. In this paper, a was designed and implemented for quality prediction and maturity level classification of Philippine Cavendish bananas. Materials and Methods The quality changes of bananas in different stages were analyzed. Twelve light intensity reflectance values for each maturity stage were compared to conventionally measured SSC, FM, PH, and color space. Results Our device can be compared with traditional forms of quality measurement. The experimental results show that the established predictive model with specific preprocessing and modeling algorithms can effectively determine various banana quality parameters (SSC, pH, FM, L*, a*, and b*). The RPD values of SSC and a* were greater than 3.0, the RPD values of L* and b* were between 2.5 and 3.0, and the pH and FM were between 2.0 and 2.5. In addition, a new banana maturity level classification method (FSC) was proposed, and the results showed that the method could effectively classify the maturity level classes (i.e. four maturity levels) with an accuracy rate of up to 97.5%. Finally, the MLR and FSC models are imported into the MCU to realize the near-range and long-range real-time display of data. Conclusions These methods can also be applied more broadly to fruit quality detection, providing a basic framework for future research.