Research on Personalized Recommendation System for Crop Cultivation

This paper introduces a personalized crop recommendation system using ensemble learning and collaborative filtering algorithm to tackle traditional cultivation’s reliance on experience and low economic returns. A soft voting ensemble model combining KNN, SVM, and RF boosts recommendation accuracy to 99.13 , and alleviates the cold start issue. An Intelligent Integrated Scoring Mechanism merges collaborative filtering scores with market price scores in a 1:1 ratio, producing a ranked crop list and an Intelligent Integrated Recommendation Score, further increasing accuracy to 99.27 and achieving Pareto optimality between yield and economic bene-fits. Experiments show the system improves the F1 score by 7.2 and 2.1 over KNN and SVM baselines, respectively, and raises the NDCG metric by 16 compared to collaborative filtering algorithm, enhancing recommendation quality and farmers’ economic outcomes.