GeaGrow: A Mobile Tool for Soil Nutrient Prediction and Fertilizer Optimization Using Artificial Neural Networks

Olusegun Folorunso ^1* Oluwafolake Ojo ² Mutiu Busari ¹ Muftau Adebayo ³ Joshua Adejumobi ¹ Daniel Folorunso ¹ Femi Ayo ⁴ Orobosade Alabi ¹ Olusola OLABANJO ¹

• ¹ Federal University of Agriculture, Abeokuta, Abeokuta, Ogun, Nigeria
• ² Osun State University, Oshogbo, Osun, Nigeria
• ³ Safefood Africa Agroenterprice, Abeokuta, Nigeria
• ⁴ Olabisi Onabanjo University, Ago Iwoye, Nigeria

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 time-consuming 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).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 preprocessed 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%.The results highlight the effectiveness of combining soil texture with water retention, NPK, and Organic Carbon to predict pH and optimize fertilizer application. The GeaGrow app provides farmers with accessible, locationbased soil insights and personalized crop recommendations, marking a significant advancement in agricultural technology. The GeaGrow app also provides smallholder farmers with scalable, ease of adoption and use of the developed mobile application.This research demonstrates the potential of ML to transform soil nutrient management and improve crop yields, contributing to sustainable farming practices in Nigeria.