Sustainability-driven fertilizer recommender system for coffee crops using case-based reasoning approach

Introduction In recent years, the increased demand for food has prompted farmers to increase production to support economic expansion. However, the excessive use of mineral fertilizers poses a significant threat to the sustainability of food systems. In Colombia, coffee cultivation plays a fundamental role in the economy, thus creating a recognized demand to elevate its production while minimizing its environmental impact sustainably. Methodology The study follows the CRISP-DM methodology (Cross-Industry Standard Process for Data Mining) developing of a fertilizer recommender system (FRS) for coffee crops. This process includes business understanding, where the key factors influencing coffee production were identified; data understanding and preparation, where agroclimatic data and expert knowledge were collected and processed; modeling, which involved building a case-based reasoning (CBR) system to recommend fertilizer doses and frequencies, and evaluation, where expert feedback was gathered to assess the system's performance. The CBR system integrates soil, crop, and climate variables to provide tailored recommendations for nitrogen, phosphorus, and potassium applications. Results The results revealed that the FRS was deemed acceptable for application in the region, with expert evaluations rating the recommendations based on their experience and knowledge. Additionally, valuable feedback was provided to facilitate future enhancements to the system. Discussion Based on expert feedback and system performance, the proposed FRS meets the minimum requirements for deployment in real crops, serving as a valuable tool for small-scale farmers. Future work will expand the case base and refine recommender algorithms to improve accuracy and usability.