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

Yogesh Shahare, Mukund Partap Singh, Prabhishek Singh, Manoj Diwakar, Vijendra Singh, Seifedine Kadry and Lukas Sevcik ()
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Yogesh Shahare: Department of Information Technology, Mahatma Gandhi Mission’s College of Engineering and Technology (MGMCET), Navi Mumbai 410 209, India
Mukund Partap Singh: School of Computer Science & Engineering Technology, Bennett University, Greater Noida 201310, India
Prabhishek Singh: School of Computer Science & Engineering Technology, Bennett University, Greater Noida 201310, India
Manoj Diwakar: Computer Science and Engineering Department, Graphic Era (Deemed to be University), Dehradun 248002, India
Vijendra Singh: School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
Lukas Sevcik: University of Zilina, 010 26 Zilina, Slovakia

Agriculture, 2023, vol. 13, issue 7, 1-18

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.

Keywords: soil organic matter (SOM); soil enzyme activity (SEA); soil organic carbon (SOC); physical soil features; chemical soil features; machine learning (ML); Artificial Neural Network (ANN) (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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