Agricultural yields have significantly decreased over the years due to soil degradation and weather variability. Inefficient utilization of these farming resources has worsened the situation. To address this challenge, this study developed an AI-driven crop recommendation framework based on structured soil and climate data. The dataset comprises key features such as temperature, humidity, and rainfall, as well as soil pH, nitrogen, phosphorus, and potassium, collected from the Wannune axis of Tarka Local Government Area in Benue State, Nigeria. To handle the data imbalance in the dataset, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied. Recursive Feature Elimination (RFE) was employed to optimize feature selection. Five tree-based models, including Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, and Extreme Gradient Boost (XGBoost), were evaluated. The XGBoost model yielded the best performance, with an accuracy of 99.65%. The use of indigenous data bridges the gap between reliability and the relevance of localization in precision agriculture. Future work includes integrating dynamic environmental variables and conducting field validation to improve scalability and adoption

Learners are continually faced with choosing appropriate courses or making career choices due to increased educational opportunities. The emergence of machine learning-based course and career recommender systems has the potential to address this issue, offering personalized course recommendations tailored to individual learning pathways, preferences, and learning history. The optimization and feature engineering techniques and practical deployment environments have not been collectively examined in the previous research, despite the significant advancements in this area of research. Furthermore, previous research has rarely synthesized how these technical components help students choose appropriate courses and careers. This systematic review was carried out to investigate the current state of machine learning-based course and career recommender systems, focusing on key elements, such as primary data sources, feature engineering methods, algorithms, optimization techniques, evaluation metrics, and the environments where the existing course recommendation models are deployed. The PRISMA method for conducting a systematic review was used to choose studies that met the requirements for inclusion and exclusion. The study findings show significant reliance on interpretable and traditional machine learning algorithms, such as K-Nearest Neighbor and Random Forest, to develop recommender models. Feature engineering remains basic, as most studies rely on normalization, while optimization processes are often underreported. Also, evaluation metrics varied widely, impeding comparability, while most of the recommender models are deployed in an e-learning environment, leaving the traditional learning environment underrepresented. Furthermore, the study findings identified issues including data sparsity and diversity, data security and privacy, and changes in learner preferences that may have an impact on the performance of recommender systems while recommending further studies to make use of standardized optimization methods, and automated domain-informed feature engineering frameworks, benchmark and annotated datasets in developing models the gives priority to learners’ success and educational relevance.