Empowering Precise Crop Recommendation System by Accompanying Tree Covariance Matrix-Parallel Random Forest Classifier

Transformation in crop management systems, particularly in creating an environment that gives rise to sustainable farming, is achieved due to innovation and the advancement of modernized agricultural technology. Anyhow, meeting the increasing food demand is one of the great challenges that stand in front of the farmers. By taking into account, factors like soil, climate, and seasonality, the crop recommendation system plays a central role in providing customized guidance to the farmers. Current crop recommendation models are often confined by a paucity of feature selection, spatial-temporal integration shortfalls, and a finite amount of decision-tree diversity. All these shortfalls retrain their scalability and accuracy. To overcome the aforementioned blocks, an innovative framework is projected that includes the Best Incremental Random Subset (BIRS) feature selection method for choosing the best features and the Parallel Random Forest (PRF) -Tree Covariance Matrix model (PRF-TCM) encourages decision-tree diversity, permitting more accurate and efficient crop recommendations. Experimental results reveal that the proposed framework outperforms existing models with accuracy (89.7), precision (88.6), and recall (87.5). The framework shows significant improvements over current models, responsible for more viable agricultural practices.