Hybridized Rule-Based Recommendation System for Sustainable and Synthetic Fertilizers in Coffee Plantation

Fertilizers are necessary for coffee cultivation, however excessive use of synthetic and sustainable fertilizers may result in adverse natural consequences, including water contamination and soil deterioration. The recommendation system will assist farmers in measuring and employing correct and appropriate fertilizers, which will help to safeguard the environment. The proposed model examined the advantages and disadvantages of natural and synthetic fertilizers. It also examines the challenges of employing sustainable fertilizers and the possibilities for raising crop yields and improving soil health through a Hybridized Rule-Based Recommendation System. Collaborative Filtering was used to analyze prior fertilizer purchases, crop selection, and geography. Knowledge-Based Filtering examined the nutrient content of fertilizers, their environmental impact, and the cost of fertilizers. Rule-based recommendations use a set of guidelines to identify the best fertilizer for a certain crop. When compared to the indicated model's machine learning approach, the method was 91.95% accurate in proposing the amount of fertilizer. As a result, the proposed strategy has the potential to increase fertilizer recommendation accuracy in precision agriculture.