eprintid: 17272
rev_number: 8
eprint_status: archive
userid: 2
dir: disk0/00/01/72/72
datestamp: 2025-03-17 23:30:10
lastmod: 2025-03-17 23:30:11
status_changed: 2025-03-17 23:30:10
type: article
metadata_visibility: show
creators_name: Afzal, Hadeeqa
creators_name: Amjad, Madiha
creators_name: Raza, Ali
creators_name: Munir, Kashif
creators_name: Gracia Villar, Santos
creators_name: Dzul López, Luis Alonso
creators_name: Ashraf, Imran
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: santos.gracia@uneatlantico.es
creators_id: luis.dzul@uneatlantico.es
creators_id: 
title: Incorporating soil information with machine learning for crop recommendation to improve agricultural output
ispublished: pub
subjects: uneat_eng
divisions: uneatlantico_produccion_cientifica
divisions: uninimx_produccion_cientifica
divisions: unic_materiales_docentes
divisions: uniromana_produccion_cientifica
full_text_status: public
keywords: Agricultural economics, Smart agriculture, Soil parameters, Crop recommendation, Smart farming, Machine learning, Ensemble model
abstract: The agriculture field is the basis of a country’s change and financial system. Crops are the main source of revenue for the people. One of the farmer’s most challenging problems is choosing the right crops for their land. This critical decision has a direct impact on productivity and profit. Wrong crop selection not only reduces yields but also causes food shortages, creating more problems for farmers. The best crop depends on many parameters such as illustration humidity, N, K, P, pH, rainfall, and temperature of the soil. Getting advice from experts is not an easy task. This requires intelligent models in crop recommendations that use machine-learning models to suggest suitable crops for soil and other environmental conditions. Temperature, humidity, and pH are important data for growing crops in agriculture. In this study, we gather and preprocess relevant data. To recommend the most suitable crop, we propose a novel ensemble learning approach called RFXG based on random forest (RF) and extreme gradient boosting (XGB) to suggest the best crop out of the twenty-two major crops. To measure the capability of the proposed approach, various machine learning models are utilized including extra tree classifier, multilayer perceptron, RF, decision trees, logistic regression, and XGB classifiers. To get the best performance, optimization of hyperparameter, and K-fold cross-validation procedures are performed. Experimental outcomes show that the proposed RFXG technique achieves a recommendation accuracy is 98%. Specifically, the proposed solution provides immediate recommendations to help farmers make timely decisions.
date: 2025-03
publication: Scientific Reports
volume: 15
number: 1
id_number: doi:10.1038/s41598-025-88676-z
refereed: TRUE
issn: 2045-2322
official_url: http://doi.org/10.1038/s41598-025-88676-z
access: open
language: en
citation:   Artículo Materias > Ingeniería <http://repositorio.unini.edu.mx/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html>
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros <http://repositorio.unini.edu.mx/view/divisions/uninimx=5Fproduccion=5Fcientifica.html>
Universidad Internacional do Cuanza > Docencia > Materiales Docentes <http://repositorio.unini.edu.mx/view/divisions/unic=5Fmateriales=5Fdocentes.html>
Universidad de La Romana > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uniromana=5Fproduccion=5Fcientifica.html> Abierto Inglés The agriculture field is the basis of a country’s change and financial system. Crops are the main source of revenue for the people. One of the farmer’s most challenging problems is choosing the right crops for their land. This critical decision has a direct impact on productivity and profit. Wrong crop selection not only reduces yields but also causes food shortages, creating more problems for farmers. The best crop depends on many parameters such as illustration humidity, N, K, P, pH, rainfall, and temperature of the soil. Getting advice from experts is not an easy task. This requires intelligent models in crop recommendations that use machine-learning models to suggest suitable crops for soil and other environmental conditions. Temperature, humidity, and pH are important data for growing crops in agriculture. In this study, we gather and preprocess relevant data. To recommend the most suitable crop, we propose a novel ensemble learning approach called RFXG based on random forest (RF) and extreme gradient boosting (XGB) to suggest the best crop out of the twenty-two major crops. To measure the capability of the proposed approach, various machine learning models are utilized including extra tree classifier, multilayer perceptron, RF, decision trees, logistic regression, and XGB classifiers. To get the best performance, optimization of hyperparameter, and K-fold cross-validation procedures are performed. Experimental outcomes show that the proposed RFXG technique achieves a recommendation accuracy is 98%. Specifically, the proposed solution provides immediate recommendations to help farmers make timely decisions. metadata Afzal, Hadeeqa; Amjad, Madiha; Raza, Ali; Munir, Kashif; Gracia Villar, Santos; Dzul López, Luis Alonso y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR     <http://repositorio.unini.edu.mx/id/eprint/17272/1/s41598-025-88676-z.pdf>     (2025) Incorporating soil information with machine learning for crop recommendation to improve agricultural output.  Scientific Reports, 15 (1).   ISSN 2045-2322     
document_url: http://repositorio.unini.edu.mx/id/eprint/17272/1/s41598-025-88676-z.pdf