Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization

Charles Onyeka Nwamekwe; Nnamdi Vitalis, Ewuzie; C. Okpala; Okechukwu Chiedu Ezeanyim; Chibuzo Victoria Nwabueze; Emeka Celestine Nwabunwanne

Article Details

Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization

Authors:

Charles Onyeka Nwamekwe | Nnamdi Vitalis, Ewuzie | C. Okpala | Okechukwu Chiedu Ezeanyim | Chibuzo Victoria Nwabueze | Emeka Celestine Nwabunwanne

Publisher:

Gazi University

Main Topics:

Agricultural Systems Analysis and Modelling | Genetically Modified Field Crops and Pasture

Journal:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.36-60

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001342772

pISSN:

2147-9542

Keywords:

Abstract:

 Soil fertility is a critical determinant of agricultural productivity, yet traditional assessment methods often fall short in providing timely and precise recommendations. This study explores the potential of machine learning ML models to predict soil fertility, leveraging localized soil data and advanced feature engineering techniques. A comprehensive methodology was employed, involving data preprocessing, feature selection, and the implementation of six ML algorithms: Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, K-Nearest Neighbours Regressor, and Neural Network MLP. The models were evaluated using robust metrics such as RMSE, R², and K-Fold Cross-Validation. Results demonstrate that engineered features significantly enhanced model performance, with Random Forest Regressor consistently outperforming other models across multiple soil nutrient parameters, achieving a testing R² of up to 0.99 and minimal RMSE. Exploratory Data Analysis EDA revealed key insights into soil nutrient dynamics, emphasizing the importance of pH, nitrogen, and organic matter as predictors. Feature engineering techniques, such as polynomial generation and scaling, further improved model accuracy and stability. This study highlights the transformative potential of ML in optimizing soil management practices. By integrating localized data and advanced predictive models, the findings provide actionable insights for farmers and agronomists, fostering sustainable agricultural practices and informed decision-making. This approach underscores the value of data-driven methods in addressing soil fertility challenges, paving the way for scalable and cost-effective solutions in precision agriculture.

 Soil fertility is a critical determinant of agricultural productivity, yet traditional assessment methods often fall short in providing timely and precise recommendations. This study explores the potential of machine learning ML models to predict soil fertility, leveraging localized soil data and advanced feature engineering techniques. A comprehensive methodology was employed, involving data preprocessing, feature selection, and the implementation of six ML algorithms: Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, K-Nearest Neighbours Regressor, and Neural Network MLP. The models were evaluated using robust metrics such as RMSE, R², and K-Fold Cross-Validation. Results demonstrate that engineered features significantly enhanced model performance, with Random Forest Regressor consistently outperforming other models across multiple soil nutrient parameters, achieving a testing R² of up to 0.99 and minimal RMSE. Exploratory Data Analysis EDA revealed key insights into soil nutrient dynamics, emphasizing the importance of pH, nitrogen, and organic matter as predictors. Feature engineering techniques, such as polynomial generation and scaling, further improved model accuracy and stability. This study highlights the transformative potential of ML in optimizing soil management practices. By integrating localized data and advanced predictive models, the findings provide actionable insights for farmers and agronomists, fostering sustainable agricultural practices and informed decision-making. This approach underscores the value of data-driven methods in addressing soil fertility challenges, paving the way for scalable and cost-effective solutions in precision agriculture.

 Soil fertility is a critical determinant of agricultural productivity, yet traditional assessment methods often fall short in providing timely and precise recommendations. This study explores the potential of machine learning ML models to predict soil fertility, leveraging localized soil data and advanced feature engineering techniques. A comprehensive methodology was employed, involving data preprocessing, feature selection, and the implementation of six ML algorithms: Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, K-Nearest Neighbours Regressor, and Neural Network MLP. The models were evaluated using robust metrics such as RMSE, R², and K-Fold Cross-Validation. Results demonstrate that engineered features significantly enhanced model performance, with Random Forest Regressor consistently outperforming other models across multiple soil nutrient parameters, achieving a testing R² of up to 0.99 and minimal RMSE. Exploratory Data Analysis EDA revealed key insights into soil nutrient dynamics, emphasizing the importance of pH, nitrogen, and organic matter as predictors. Feature engineering techniques, such as polynomial generation and scaling, further improved model accuracy and stability. This study highlights the transformative potential of ML in optimizing soil management practices. By integrating localized data and advanced predictive models, the findings provide actionable insights for farmers and agronomists, fostering sustainable agricultural practices and informed decision-making. This approach underscores the value of data-driven methods in addressing soil fertility challenges, paving the way for scalable and cost-effective solutions in precision agriculture.

 Soil fertility is a critical determinant of agricultural productivity, yet traditional assessment methods often fall short in providing timely and precise recommendations. This study explores the potential of machine learning ML models to predict soil fertility, leveraging localized soil data and advanced feature engineering techniques. A comprehensive methodology was employed, involving data preprocessing, feature selection, and the implementation of six ML algorithms: Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, K-Nearest Neighbours Regressor, and Neural Network MLP. The models were evaluated using robust metrics such as RMSE, R², and K-Fold Cross-Validation. Results demonstrate that engineered features significantly enhanced model performance, with Random Forest Regressor consistently outperforming other models across multiple soil nutrient parameters, achieving a testing R² of up to 0.99 and minimal RMSE. Exploratory Data Analysis EDA revealed key insights into soil nutrient dynamics, emphasizing the importance of pH, nitrogen, and organic matter as predictors. Feature engineering techniques, such as polynomial generation and scaling, further improved model accuracy and stability. This study highlights the transformative potential of ML in optimizing soil management practices. By integrating localized data and advanced predictive models, the findings provide actionable insights for farmers and agronomists, fostering sustainable agricultural practices and informed decision-making. This approach underscores the value of data-driven methods in addressing soil fertility challenges, paving the way for scalable and cost-effective solutions in precision agriculture.

Article Details

Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization

Authors:

Charles Onyeka Nwamekwe | Nnamdi Vitalis, Ewuzie | C. Okpala | Okechukwu Chiedu Ezeanyim | Chibuzo Victoria Nwabueze | Emeka Celestine Nwabunwanne

Publisher:

Gazi University

Main Topics:

Agricultural Systems Analysis and Modelling | Genetically Modified Field Crops and Pasture

Journal:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.36-60

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001342772

pISSN:

2147-9542

Soil Fertility

Machine Learning

Feature Engineering

Predictive Modelling

Agricultural Optimization

Issues

Article Details

Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization

Authors: Charles Onyeka Nwamekwe | Nnamdi Vitalis, Ewuzie | C. Okpala | Okechukwu Chiedu Ezeanyim | Chibuzo Victoria Nwabueze | Emeka Celestine Nwabunwanne

Publisher: Gazi University

Main Topics: Agricultural Systems Analysis and Modelling | Genetically Modified Field Crops and Pasture

Journal: Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.36-60

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001342772

pISSN: 2147-9542

Soil Fertility

Machine Learning

Feature Engineering

Predictive Modelling

Agricultural Optimization

Issues

Authors:

Charles Onyeka Nwamekwe | Nnamdi Vitalis, Ewuzie | C. Okpala | Okechukwu Chiedu Ezeanyim | Chibuzo Victoria Nwabueze | Emeka Celestine Nwabunwanne

Publisher:

Gazi University

Main Topics:

Agricultural Systems Analysis and Modelling | Genetically Modified Field Crops and Pasture

Journal:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.36-60

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001342772

pISSN:

2147-9542