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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Ahmed Mohammed Gimba
Sharda University
Computer Science & IT Engineering

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A hybrid machine learning model for optimized mixed-crop recommendation Ahmed Mohammed Gimba; Pradeep Kumar Mishra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2314-2324

Abstract

Farmers today encounter more challenges when selecting appropriate variety of crops depending on their farm soil nutrients and climate. This research will assist farmers in choosing suitable mixed-crops depending on the individual farms soil and climate conditions in Andhra Pradesh, India. Using the dataset sourced from Indian Institute of Soil Science (IISS), Bhopal with 2,552 entries. Previous studies focused on only single-crop recommendations. This work proposes a novel hybrid mixed-crop recommendation system (CRS) that incorporates several machine learning (ML) techniques comprise of random forest-ExtraTrees (RF-ExtraTrees), decision tree-C4.5 (DT-C4.5), extreme gradient boosting-gradient boosting (XGBoost-GBoost), quadratic discriminant analysis-linear discriminant analysis (QDA-LDA), and support vector machine-stochastic gradient descent (SVM-SGD) were utilized to recommend mixed-crops. To enhance the reliability of the training process, 20% of the dataset was held in reserve for validation to analyze model performance. The result of the proposed work shows that all the hybrid ML models applied were viable, and RF ExtraTrees has achieved 95.91% best accuracy, 95.08% precision, and 95.91% recall, when contrasted to the other ML models.
Leveraging Ensemble Learning Technique for Efficient Fertilizer Recommendation Ahmed Mohammed Gimba; Pradeep Kumar Mishra
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 4 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.4.509

Abstract

Agricultural productivity plays a critical role in improving crop yields, and the suitable use of fertilizers plays a significant role in enhancing crop yield. Traditional fertilizer recommendation approaches often rely on generalized strategies that may not account for discrepancies in soil properties, climatic conditions. To address this limitation, we proposed an intelligent Fertilizer Recommendation System (FRS) using an Ensemble Learning method. This system integrates multiple ensemble learning models, such as Bagging, AdaBoost, GBoosting, Extra Trees, and CatBoost to enhance recommendation accuracy. The ensemble model is trained on soil parameters (N) nitrogen (P) phosphorus, (K) potassium, and moisture to recommend the optimal fertilizer type in Andhra Pradesh region, India. The result shows that all ensemble models utilized were effective, and CatBoost model has achieved 94.78% with highest accuracy, when compared with the other ensemble models.
Co-Authors Pradeep Kumar Mishra