Mountain-Specific AI System for Optimized Crop Planning and Fertilizer use

A number of studies address the application of machine learning (ML) methods for assessing soil fertility and crop yield in fragile mountain ecosystems like Yunnan Province, the Indian Himalayas, and the European Alps. ML methods including Random Forest (RF), Support Vector Regression (SVR), and ensemble models have been used to predict soil depth, spatial distributions of soil organic carbon, nitrogen, phosphorus, and potassium, as well as their associations with environmental covariates (e.g., elevation, precipitation, temperature, and terrain). The models have generally demonstrated strong predictive performance (R² of 0.85– 0.98), producing fine-resolution digital soil mapping (DSM) and reliable crop yield forecasts.