Understanding the nutrient content of soils, such as nitrogen (N), phosphorus (P), potassium (K), pH, temperature, and moisture is key to dealing with soil variation and climate uncertainty. Effective soil nutrient management can increase plant resilience to climate change as well as improve water use. In addition, soil nutrients affect the selection of suitable plant types, considering that each plant has different nutritional needs. However, the lack of integration of soil nutrient analysis in agricultural practices leads to the inefficient use of inputs, impacting crop yields and environmental sustainability. This study aims to propose a soil nutrient assessment scheme that can recommend plant types using ensemble learning and remote sensing. Remote sensing proposals support performance broadly, while ensemble learning is helpful for precision agriculture. The results of this scheme show that the nutrient assessment with remote sensing provides an opportunity to evaluate soil conditions and select suitable plants based on the extraction of N, P, K, pH, TCI, and NDTI values. Then, Ensemble Learning algorithms such as Random Forest work more dominantly compared to XGBoost, AdaBoost, and Gradient Boosting, with an accuracy level of 0.977 and a precision of 0.980 in 0.895 seconds.