Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review

Olusegun Folorunso; Oluwafolake Ojo; Mutiu Busari; Muftau Adebayo; Adejumobi Joshua; Daniel Folorunso; Charles Okechukwu Ugwunna; Olufemi Olabanjo; Olusola Olabanjo

doi:10.3390/bdcc7020113

journal article Open Access Jun 08, 2023

Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review

Olusegun Folorunso

Oluwafolake Ojo

Mutiu Busari Muftau Adebayo Adejumobi Joshua Daniel Folorunso Charles Okechukwu Ugwunna Olufemi Olabanjo Olusola Olabanjo

Big Data and Cognitive Computing Vol. 7 No. 2 pp. 113 · MDPI AG

View at Publisher Save 10.3390/bdcc7020113

Abstract

Agriculture is essential to a flourishing economy. Although soil is essential for sustainable food production, its quality can decline as cultivation becomes more intensive and demand increases. The importance of healthy soil cannot be overstated, as a lack of nutrients can significantly lower crop yield. Smart soil prediction and digital soil mapping offer accurate data on soil nutrient distribution needed for precision agriculture. Machine learning techniques are now driving intelligent soil prediction systems. This article provides a comprehensive analysis of the use of machine learning in predicting soil qualities. The components and qualities of soil, the prediction of soil parameters, the existing soil dataset, the soil map, the effect of soil nutrients on crop growth, as well as the soil information system, are the key subjects under inquiry. Smart agriculture, as exemplified by this study, can improve food quality and productivity.

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Details

Published: Jun 08, 2023
Vol/Issue: 7(2)
Pages: 113
License: View

Authors

O

Olusegun Folorunso

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

O

Oluwafolake Ojo

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

M

Mutiu Busari

Department of Soil Science and Land Management, Federal University of Agriculture, Abeokuta 2240, Nigeria

M

Muftau Adebayo

Safefood Africa Agroenterprise, Abeokuta 110123, Nigeria

A

Adejumobi Joshua

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

D

Daniel Folorunso

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

C

Charles Okechukwu Ugwunna

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

O

Olufemi Olabanjo

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

O

Olusola Olabanjo

Department of Computer Science, Federal University of Agriculture, Abeokuta 2240, Nigeria

Funding

European Union Award: FED/2020/420-491

Cite This Article

Olusegun Folorunso, Oluwafolake Ojo, Mutiu Busari, et al. (2023). Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review. Big Data and Cognitive Computing, 7(2), 113. https://doi.org/10.3390/bdcc7020113

Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review

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