[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122416-en":3,"doc-seo-122416-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122416,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru","Remote sensing supports precision agriculture by enabling high-resolution characterization of soil physical and chemical properties for informed decision making. This study assesses soil fertility changes by comparing them with prior fertilization inputs using multispectral UAV imagery and in situ measurements. A UAV image was used to predict soil parameter spatial patterns by computing fourteen spectral indices and a digital surface model from 103 plots over 49.83 hectares. Machine learning models, especially random forest, estimated N, P, K, OM, and EC and revealed strong spatiotemporal shifts between 2022 and 2023.","Article  \nDetecting Changes in Soil Fertility Properties Using  \nMultispectral UAV Images and Machine Learning in Central Peru  \nLucia Enriquez 1,2, Kevin Ortega 2,3, Dennis Ccopi 2,4, Claudia Rios 1, Julio Urquizo 1, Solanch Patricio 1, Lidiana Alejandro 4, Manuel Oliva-Cruz 5, *, Elgar Barboza 5 and Samuel Pizarro 4,5, *  \nAcademic Editor: Leonardo Conti  \nReceived: 19 December 2024  \nRevised: 27 February 2025  \nAccepted: 28 February 2025  \nPublished: 6 March 2025  \nCitation: Enriquez, L.; Ortega, K.; Ccopi, D.; Rios, C.; Urquizo, J.; Patricio, S.; Alejandro, L.; Oliva-Cruz, M.; Barboza, E.; Pizarro, S. Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru. AgriEngineering 2025, 7, 70 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)agriengineering7030070  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Estación Experimental Agraria Santa Ana, Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande-Hualahoyo Km 8 Santa Ana, Huancayo 12000, Junin, Peru; [luciacep7@gmail.com](luciacep7@gmail.com) (L.E.); [claudiario090@gmail.com](claudiario090@gmail.com) (C.R.); [juliocesarub3@gmail.com](juliocesarub3@gmail.com) (J.U.); [solanch.patricio.r@gmail.com](solanch.patricio.r@gmail.com) (S.P.)  \n2 Research Group on Nature Conservation and Climate Change, Facultad de Ciencias Forestales y del Ambiente, Universidad Nacional del Centro del Perú, Av. Mariscal Castilla 3909, Huancayo 12006, Junin, Peru; [kevinorqu@gmail.com](kevinorqu@gmail.com) (K.O.); [dennisccopit@gmail.com](dennisccopit@gmail.com) (D.C.)  \n3 Estación Experimental Agraria Santa Ana, Dirección de Recursos Genéticos y Biotecnología, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande-Hualahoyo Km 8 Santa Ana, Huancayo 12000, Junin, Peru  \n4 Estación Experimental Agraria Santa Ana, Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande-Hualahoyo Km 8 Santa Ana, Huancayo 12000, Junin, Peru; [lidiana.alejandro@gmail.com](lidiana.alejandro@gmail.com)  \n5 Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas 01001, Amazonas, Peru; [ebarboza@indes-ces.edu.pe](ebarboza@indes-ces.edu.pe)  \n* [Correspondence: manuel.oliva@untrm.edu.pe](Correspondence: manuel.oliva@untrm.edu.pe) (M.O.-C.); [samuel.pizarro@untrm.edu.pe](samuel.pizarro@untrm.edu.pe) (S.P.); Tel.: +51-963952025 (S.P.)  \nAbstract: Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil’s physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m) . The RF model outperformed others, with R2 values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87","cbCaiqc9CgJpWUZO","https://ap.wps.com/l/cbCaiqc9CgJpWUZO","pdf",2910146,1,18,"English","en",105,"# Introduction\n## Soil formation and human influence\n## Indicators for assessing soil fertility\n## Need for monitoring fertility changes\n## Study context in the Andean valley highlands","[{\"question\":\"How does the study estimate soil fertility parameters from UAV data?\",\"answer\":\"It uses a UAV-captured multispectral image to generate fourteen spectral indices and a digital surface model, then applies machine learning to predict soil parameters across the study plots.\"},{\"question\":\"Which machine learning approach performed best in predicting soil properties?\",\"answer\":\"Random forest outperformed the other models, achieving reported R2 values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023.\"},{\"question\":\"What changes in soil fertility were observed between 2022 and 2023?\",\"answer\":\"Significant spatiotemporal variations were detected, including an increase in P (14.87 ppm) and a reduction in EC (−0.954 mS/m).\"}]","Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru | PDF",1785810521,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detecting-changes-in-soil-fertility-properties-using-multispectral-uav-images-and-machine-learning-in-central-peru","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detecting-changes-in-soil-fertility-properties-using-multispectral-uav-images-and-machine-learning-in-central-peru/122416/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How 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