A Comprehensive Analysis of Machine Learning-Based Assessment and Prediction of Soil Enzyme Activity

Different soil characteristics in different parts of India affect agriculture growth. Crop growth and crop production are significantly impacted by healthy soil. Soil enzymes mediate almost all biochemical reactions the Understanding biological processes carbon nitrogen cycling requires defining significance prospective elements at play evaluating their activities. A combination Multiple Linear Regression (MLR), Random Forest (RF) models, Artificial Neural Networks (ANN) was employed this study to assess enzyme activity, including amylase urease physical properties, such as sand, silt, clay, chemical organic matter (SOM), (N), phosphorus (P), (SOC), pH, fertility level. Compared other methods for estimating phosphatase, cellulose, RF model outperforms MLR model. In addition, due its ability manage dynamic hierarchical relationships between activities, models activity. This collected 3972 samples from 25 villages Bhandara district Maharashtra, India, with chemical, physical, parameters. Overall, 99% accuracy achieved cellulase activity 94% N-acetyl-glucosaminidase using Crops have been suggested based on best performance algorithms evaluation metrics.