A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms

Background: The application of base fertilizer is significant for reducing agricultural costs, non-point source pollution, and increasing crop production. However, the existing fertilization decision methods require many field observations and have high prices for popularization and application. Methods: This study proposes an innovative model integrating machine learning (ML) and swarm intelligence search algorithms to overcome the above issues. Based on historical data for maize, rice, and soybean crops, ML algorithms including random forest (RF), extreme random tree (ERT), and extreme gradient boosting (XGBoost) were evaluated for predicting crop yield. Coupled with the cuckoo search algorithm (CSA), the prime fertilization decision model (FDM) was established to discover the optimal fertilization strategy. Result: For all three crops, the yield simulation accuracy of the ERT model was the highest, with an R-2 and RRMSE of 0.749, 0.775, and 0.744, and 0.086, 0.051, and 0.078, respectively. Considering soil nutrient and fertilization characteristics as the determinants of yield and optimizing fertilization strategies, the proposed model can increase the average yield of maize, rice, and soybean in the study area by 23.9%, 13.3%, and 20.3%, respectively. Conclusions: The coupling model of ERT and the CSA constructed in this study can be used for the intelligent and rapid decision-making of the base fertilizer application for crops considered in the present study.