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

Abstract

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

A longitudinal analysis of soil salinity changes using remotely sensed imageries

“…Regression analysis is a commonly used statistical tool to study relationships between factors, making it straightforward to analyze multifactor data 54 . This study employs SVR 44 , 55 – 59 , RF 22 , 58 , 60 – 62 , DT 63 , 64 , and the XGBoost 16 , 65 – 68 methods to investigate the association between remotely sensed imagery data and field soil salinity. The Scikit-learn for Python (version 1.3.0) is used to implement these algorithms 69 .…”

Application of Fuzzy Logic Controller for Measurement of Body Temperature and Heart Rate

“…It can be seen in the photo that the pulse is 1.5 ms in a period of 2 ms wide so that the angle of the motor axis will be in the middle position. Continue to increase the width of the OFF pulse until it continues to increase the axis movement in the clockwise direction and continues to become small the OFF pulse increases until it continues to increase the axis movement in the anti-clockwise direction [11].…”