Predicting growth parameters of biofertilizer inoculated pepper, using root capacitance assessments and artificial neural networks in two soils.

Abstract

Monitoring the root system plays an important role in understanding plant physiological processes; however, its assessment using non-destructive methods remains challenging. Here, we evaluate the utility of root capacitance (C_R) as a practical indicator of root function and its relationship to plant growth parameters in Capsicum annuum L. To improve the accuracy of root function assessment, we applied artificial neural networks (ANN) as a novel data evaluation approach, comparing its predictive performance against multiple linear regression (MLR). Across two soil types (sandy and sandy loam), we applied multiple treatments ranging from microbial inoculants to wool pellet and inorganic nitrogen sources primarily to test whether C_R could detect differences in root activity and biomass production under different conditions. We measured root dry biomass, shoot dry biomass, and leaf N content, treating these variables as independent predictors in a statistical framework. Multiple linear regression (MLR) initially showed strong relationship between C_R and both root and shoot biomass in sandy soil, and between C_R and total plant N content in sandy loam. However, an ANN model consistently outperformed MLR in predicting C_R from plant physiological parameters, as evidenced by lower mean absolute error (MAE) in all treatments. These findings confirm that C_R correlates strongly with plant growth parameters and can reliably distinguish the effects of different soil amendments even those with markedly different nutrient-release profiles.