Response surface methodology and artificial neural network modelling for enhancing maturity parameters during vermicomposting of floral waste

In this study, the mixture of floral waste and cattle dung in different proportions was utilised to convert into vermicompost using earthworm Eisenia fetida. In the design of the experiment, the optimum amount of floral waste (1325 g) and cattle dung (500 g) was obtained for vermicompost using central composite design (CCD) and compared with the output of artificial neural network (ANN). The optimum proportions of vermicompost showed pH of 7.10, electrical conductivity of 3.39 mS/cm, total organic carbon of 34.01%, C: N ratio of 13, phosphorous of 5.31 g/kg and potassium of 14.45 g/kg. This vermicompost was enriched with sufficient concentration of nutrients like potassium, sodium, phosphorous, and calcium, which are beneficial for the growth of the plants. The current study was based on comparing response surface methodology (RSM) and ANN for maturity parameters and the value of R2 in both the cases was near 1.

Automated summary

This study utilized floral waste and cattle dung to produce vermicompost rich in nutrients, with the optimal proportions determined through central composite design and compared with artificial neural network results. The resulting vermicompost was found to be beneficial for plant growth. The study compared response surface methodology and ANN for maturity parameters, with R2 values close to 1 in both cases.