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.

Plant phenotyping relevance

根キャパシタンスを根機能・バイオマスの非破壊指標として評価し、ANNによる予測性能をMLRと比較しているため、処理試験にとどまらずフェノタイピング手法の実用性・精度評価が中心的です。

Code and data availability

The article describes a pot experiment measuring root capacitance and plant growth parameters analyzed with MLR and ANN (MATLAB, R), but contains no public data deposit, no author code/model release, and no dataset availability statement. The only supplementary material is a small DOCX containing an architecture figure

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