Crop planting recommendation algorithm based on ensemble learning

In this paper, we propose a crop planting recommendation algorithm based on ensemble learning to recommend the most suitable crops for farmers based on environmental characteristics and soil element content, which can achieve scientific planting and crop yield increase. Firstly, we adjust the scaling ratio of N (nitrogen), P (phosphor) and K (potassium) elements, which play an important role in crop growth, and use KNN (K-Nearest Neighbor), XGBoost and RF (Random Forest) as weak learners, and use GA (Genetic Algorithm) to optimize the important parameters for KNN, XGBoost and RF, and combine these three weak learners to obtain the ensemble learning model through a soft voting mechanism. After training and testing on the Kaggle public dataset, the accuracy of the crop planting recommendation algorithm based on ensemble learning can reach 94.36%.