Flexible Vis/NIR wireless sensing system for banana monitoring

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