Aquatic macrophytes, notably the invasive water hyacinth, exhibit proficiency in nutrient removal from polluted water bodies, rendering them appealing for water remediation applications. This study investigates the potential of water hyacinth in phytoremediation, focusing on the effect of using nutrient-rich biogas slurry mixed with water in varying concentrations, i.e., 16.6, 33, 66.6, 100, and 133 mg/L for the investigation. The physiochemical properties of the liquid biogas slurry were evaluated before and after treatment with water hyacinth over eight weeks, with continuous monitoring of nutrient reduction rates. Results showcased substantial average reductions of nitrogen, phosphorus, and potassium, with a relative growth rate of 5.55%. The treatment also decreased pH, total dissolved solids, hardness, and chemical oxygen demand. The theoretical BMP of water hyacinth was determined using Buswell’s equation. Water hyacinth grown in the concentration of the biogas slurry exhibited the highest methane yield at 199 mL CH₄/gm VS, along with the highest relative growth rate. This study used experimental data to create a mathematical model that describes how the relative growth of water hyacinth depends on the number of days and biogas slurry concentration (C). The model’s quality and effectiveness were evaluated using the goodness of fit (R²) and observable approaches. The polynomial model, referred to as Poly model 1, 2, is the best fit for describing the relationship between the growth percentage of water hyacinth, days, and nutrient solution concentration. In this model, C has a polynomial degree of one (normalized mean of 69.84 ± 43.54), while D has a degree of two (normalized mean of 30 ± 21.65).

The present study evaluates the synergistic application of an anaerobic digestate for enhanced rice yield. The study utilised the digestate as a fertiliser with various inoculum-to-substrate (IS) ratios of anaerobic digestion from cow dung and water hyacinth (CW–BF) with combinations of NPK (16-22-22) fertiliser for rice yield optimisation. The outcome of the combined digestate and fertiliser application on rice cultivation was observed in terms of parameters such as the number of tillers, panicle number, panicle length, fertile panicles, and 1000-grain weight. The digestate combination of CW–BF:NPK (3:1:1) resulted in the highest grain yield (7521 kg/hectare) with increased panicle length, test weight, and more filled grains than the other combinations. Moreover, various machine-learning approaches were used to study the efficacy of the different combinations of applied fertiliser (cow dung, water hyacinth, and NPK). The gradient-boosting machine-learning model was appropriate for predicting the modelling based on the measured data. Principal component analysis revealed NPK as the first principal component with high loading values and the digestate as the second principal component, which indicates its crucial role in fertiliser preparation. Therefore, deploying such hybridised fertilisers using the proper statistical analysis and machine-learning approaches can improve rice yield, which would be essential for the socio-economic uplifting of marginal rice farmers.