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

• Published in: Big 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
• Language: English
• 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
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc7020113

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...