An Analysis of the Conditions that Influence Agriculture's Production: A Machine Learning Approach

Food security, economic stability, and sustainable development in agrarian economies rely greatly on the output of agriculture. The current paper has considered different environmental and economic as well as technological conditions affecting the agricultural production and also uses machine learning to simulate the effect and foretell the same. Depending on a number of variables (rain, temperature, soil quality, fertilizer application, type of crop, and market dynamics), a set of machine learning models (linear regression, decision trees, random forests, and support vector machines) can be executed and assessed. The research will be in a position to identify the optimal predictors of the crop yields and the quality and intensity of the various models in forecasting the agricultural output. The findings also demonstrate that the machine learning models, and the ensemble models in particular, will prove useful compared to the conventional statistics and procedures in dealing with the nonlinear relation and extremely dimensional data. The results indicate that data-driven interventions can be applied to optimize and make policy-making decisions associated with improving agricultural operations and criticize using precision agriculture.