Publication Type

Journal Article

Publication Date (Issue Year)

2025

Journal Name

Frontiers in Sustainable Food Systems

Abstract

Introduction: Most farmers in Nigeria lack knowledge of their farmland’s nutrient content, often relying on intuition for crop cultivation. Even when aware, they struggle to interpret soil information, leading to improper fertilizer application, which can degrade soil and ground water quality. Traditional soil nutrient analysis requires field sample collection and laboratory analysis; a tedious and timeconsuming process. Digital Soil Mapping (DSM) leverages Machine Learning (ML) to create detailed soil maps, helping mitigate nutrient depletion. Despite its growing use, existing DSM-based ML methods face challenges in prediction accuracy and data representation. Aim: This study presents GeaGrow, an innovative mobile app that enhances agricultural productivity by predicting soil properties and providing tailored fertilizer recommendations for yam, maize, cassava, upland rice, and lowland rice in southwest Nigeria using Artificial Neural Networks (ANN). Materials and methods: The presented method involved the collection of soil samples from six states in southwest Nigeria which were analysed in the laboratory to compile the primary dataset mapped to the coordinates. A secondary dataset was compiled using iSDAsoil’s API for data augmentation and validation. The two sets of data were pre-processed and normalized using Python, and an ANN was employed to predict soil properties such as NPK, Organic Carbon, Soil Textural Composition and pH levels through regressive analysis while building a composite model for Soil Texture Classification based on the predicted soil composition. The model’s performance yielded a Mean Absolute Error (MAE) of 1.9750 for NPK and Organic Carbon prediction, 3.5461 for Soil Textural Composition prediction, and 0.1029 for pH prediction. For the classification of the soil texture, the results showed a high accuracy value of 99.9585%.

Keywords

precision farming, machine learning, soil data analytics, fertilizer optimization, GeaGrow mobile application

Grantee Name(s)

Olusegun Folorunso

Project Title

Computer Science, Federal University of Agriculture Abeokuta.

Type of Grant

Grant – AGRiDI

Thematic Area

ICTs Including Big Data and Artificial Intelligence

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