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

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...

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Published inBig data and cognitive computing Vol. 7; no. 2; p. 113
Main Authors Folorunso, Olusegun, Ojo, Oluwafolake, Busari, Mutiu, Adebayo, Muftau, Joshua, Adejumobi, Folorunso, Daniel, Ugwunna, Charles Okechukwu, Olabanjo, Olufemi, Olabanjo, Olusola
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Agricultural production
Agriculture
Analysis
Artificial intelligence
Crop growth
Crop yield
Crop yields
Digital mapping
digital soil mapping
Food
Information systems
Innovations
Machine learning
Nutrients
Predictions
smart soil
Soil fertility
Soil mapping
Soil maps
Soil properties
Nigeria
Germany
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Summary: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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ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc7020113