TY  - JOUR
Y1  - 2025/03//
KW  - Agricultural economics
KW  -  Smart agriculture
KW  -  Soil parameters
KW  -  Crop recommendation
KW  -  Smart farming
KW  -  Machine learning
KW  -  Ensemble model
N2  - 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.
SN  - 2045-2322
JF  - Scientific Reports
AV  - public
VL  - 15
ID  - uneatlantico17272
A1  - Afzal, Hadeeqa
A1  - Amjad, Madiha
A1  - Raza, Ali
A1  - Munir, Kashif
A1  - Gracia Villar, Santos
A1  - Dzul López, Luis Alonso
A1  - Ashraf, Imran
UR  - http://doi.org/10.1038/s41598-025-88676-z
TI  - Incorporating soil information with machine learning for crop recommendation to improve agricultural output
IS  - 1
ER  -