GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks
IntroductionMost 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. Traditi...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Sustainable Food Systems |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsufs.2025.1533423/full |
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| author | Olusegun Folorunso Oluwafolake Ojo Mutiu Busari Muftau Adebayo Joshua Adejumobi Daniel Folorunso Femi Ayo Orobosade Alabi Olusola Olabanjo |
| author_facet | Olusegun Folorunso Oluwafolake Ojo Mutiu Busari Muftau Adebayo Joshua Adejumobi Daniel Folorunso Femi Ayo Orobosade Alabi Olusola Olabanjo |
| author_sort | Olusegun Folorunso |
| collection | DOAJ |
| description | IntroductionMost 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.AimThis 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 methodsThe 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%.ResultsThe 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, location-based 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.ConclusionThis research demonstrates the potential of ML to transform soil nutrient management and improve crop yields, contributing to sustainable farming practices in Nigeria. |
| format | Article |
| id | doaj-art-57fac88cbe7d4860bd5790f9d91d2483 |
| institution | Kabale University |
| issn | 2571-581X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Sustainable Food Systems |
| spelling | doaj-art-57fac88cbe7d4860bd5790f9d91d24832025-08-20T03:39:44ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2025-03-01910.3389/fsufs.2025.15334231533423GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networksOlusegun Folorunso0Oluwafolake Ojo1Mutiu Busari2Muftau Adebayo3Joshua Adejumobi4Daniel Folorunso5Femi Ayo6Orobosade Alabi7Olusola Olabanjo8Department of Computer Science, Federal University of Agriculture Abeokuta, Abeokuta, NigeriaDepartment of Information Technology, Osun State University, Oshogbo, NigeriaDepartment of Soil Science and Land Management, Federal University of Agriculture, Abeokuta, NigeriaSafefood Africa Agroenterprice, Abeokuta, NigeriaDepartment of Computer Science, Federal University of Agriculture Abeokuta, Abeokuta, NigeriaDepartment of Computer Science, Federal University of Agriculture Abeokuta, Abeokuta, NigeriaDepartment of Computer Science, Olabisi Onabanjo University, Ago-Iwoye, NigeriaDepartment of Computer Science, Federal University of Agriculture Abeokuta, Abeokuta, NigeriaDepartment of Computer Science, Federal University of Agriculture Abeokuta, Abeokuta, NigeriaIntroductionMost 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.AimThis 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 methodsThe 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%.ResultsThe 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, location-based 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.ConclusionThis research demonstrates the potential of ML to transform soil nutrient management and improve crop yields, contributing to sustainable farming practices in Nigeria.https://www.frontiersin.org/articles/10.3389/fsufs.2025.1533423/fullprecision farmingmachine learningsoil data analyticsfertilizer optimizationGeaGrow mobile application |
| spellingShingle | Olusegun Folorunso Oluwafolake Ojo Mutiu Busari Muftau Adebayo Joshua Adejumobi Daniel Folorunso Femi Ayo Orobosade Alabi Olusola Olabanjo GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks Frontiers in Sustainable Food Systems precision farming machine learning soil data analytics fertilizer optimization GeaGrow mobile application |
| title | GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks |
| title_full | GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks |
| title_fullStr | GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks |
| title_full_unstemmed | GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks |
| title_short | GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks |
| title_sort | geagrow a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks |
| topic | precision farming machine learning soil data analytics fertilizer optimization GeaGrow mobile application |
| url | https://www.frontiersin.org/articles/10.3389/fsufs.2025.1533423/full |
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