A Comparative Analysis of Zinc-solubilizing Bacteria-mediated Chlorophyll Content in Wheat by Artificial Neural Networks and Random Forest
Nitesh Kumar Singh, Anand Prakash Singh, Amit Kumar, Aditi Chourasia, Shashank Shekher Singh, Tej pratap
International Journal of Environment and Climate Change · pp. 484–491 · Published 18 Nov 2025
10.9734/ijecc/2025/v15i115129Abstract
This study compares Artificial Neural Network (ANN) models and Random Forest methods for predicting chlorophyll content in wheat under different zinc sources and bacterial inoculations. Experimental data from 2016–17 were analyzed. The total data set 32 were used and using four modeling approaches—two ANN architectures, Linear Regression, and Random Forest. Among them, Random Forest achieved the highest accuracy (R² = 0.83, RMSE = 1.50), outperforming ANN models due to better handling of small datasets. The results demonstrate that growth stage, zinc source, and bacterial treatment significantly influence chlorophyll levels. This study provides insights into selecting suitable modeling techniques for agricultural prediction tasks.
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