Estimation of mass, chlorophylls, and anthocyanins of Spirodela polyrhiza with smartphone acquired images

•Smartphone image gives rapid, cheap and onsite quantification for mass and pigments.•Image processing gave high accuracies with 3.1% error for mass correlation.•G and 2G-B gave only around 10% error for chlorophyll and anthocyanin prediction.•Smartphone images showed reliability in S. polyrhiza mass and pigments detection. Premium cameras may provide excellent quality images for analysis, but not everyone can afford to own one compared to a smartphone camera. Therefore, this has given rise to several attempts to incorporate smartphone and image analysis in analytical procedures. This study intended to seek the possibilities of using a smartphone to capture images and subsequent analysis to estimate mass, chlorophylls, and anthocyanin simultaneously in Spirodela polyrhiza by correlation models. This work serves as the substitution for conventional protocols in which determination of mass and phytochemicals in plants require the destruction of samples, usage of reagents, long processing time, and dependent on the availability of high-end equipment. In this study, the image taken with a smartphone camera was processed, and necessary information was extracted using ImageJ software. The area of plantlets was measured, and the relationship between area and mass was studied. Color parameters values extracted from the image were transformed into different combinations to explore the strength of the relationship between color parameters and combinations with chlorophyll and anthocyanin content of S. polyrhiza. Saturation channel from HSI color space was found to predict the mass of plantlets slightly more accurately than a* channel from L*a*b* color space. The mean green (G) value of the image was a robust parameter to predict chlorophyll contents in S. polyrhiza with a high r2 of 0.9693 and the lowest error compared to other color parameters and their combinations. Compared with other color parameters and combinations, the 2G-B value presented the most robust relationship with anthocyanin contents in S. polyrhiza¸having r2 = 0.8638 and the lowest percentage of errors. The mean G value predicts chlorophylls content in S. polyrhiza with 9.5 ± 7.3% of errors, while 2G-B estimates anthocyanins content with 10.42 ± 6.82% errors. This research demonstrated that the images captured with smartphones could be a ground-breaking strategy to predict mass, chlorophylls, and anthocyanins content in S. polyrhiza sufficiently accurate, rapid, and cost-effective compared with the