Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming

Water quality assessment is currently based on time-consuming and costly laboratory procedures and numerous expensive physicochemical sensors deployment. In response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring, the integration of aquaphotomics and computational intelligence is presented in this paper. This study used the combination of temperature, pH, and electrical conductivity sensors in predicting crop growth primary macronutrient concentration (nitrate, phosphate, and potassium (NPK)), thus, limiting the number of deployed sensors. A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36 °C with 2 °C increments to mimic ambient range, which varies water compositional structure. Aquaphotomics was applied on ultraviolet, visible light, and near-infrared spectral regions, 100 to 1 000 nm, to determine NPK compounds. Principal component analysis emphasized nutrient dynamics through selecting the highly correlated water absorption bands resulting in 250 nm, 840 nm, and 765 nm for nitrate, phosphate, and potassium respectively. These activated water bands were used as wavelength protocols to spectrophotometrically measure macronutrient concentrations. Experiments have shown that multigene symbolic regression genetic programming (MSRGP) obtained the optimal performance in parameterizing and predicting nitrate, phosphate, and potassium concentrations based on water physical properties with an accuracy of 87.63%, 88.73%, and 99.91%, respectively. The results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30 °C and phosphate below 25 °C with pH and electrical conductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm⁻¹ respectively. This novel approach of developing a physicochemical estimation model predicted macronutrient concentrations in real-time using physical limnological sensors with a 50% reduction of energy consumption. This same approach can be extended to measure secondary macronutrients and micronutrients.