Principles and Practices of Making Agriculture Sustainable: Crop Yield prediction using Random Forest

Abstract

Agriculture has advanced tremendously over the last 100 years. In fact it is been keeping up with food production at a very high rate. In fact, some scientists feel that agriculture already produces enough food to feed the world, but of course there are issues and problems with food availability, agricultural production practices, preservation and transportation, and probably more that one can think of that hinder many people in this world from getting adequate food. The basic challenge is to provide food for the needy people. This need can be fulfilled with the help of the farmers taking responsibility in increasing the food production by 50% by the year 2050. The objective of the present work is to increase this food production, protecting the environment with managing natural resources. Mainly focusing on water, nutrients and other inputs to produce foods without degrading the environment. The Goal is to develop the social, environmental, and the economic aspects of possible solutions to minimize the agricultural footprint, and become more sustainable. The dataset considered in our experiment is used in yield prediction based on historic yield and weather information. Implemented both the versions of Thomson model and compared the result with segmentation model, Random Forest (RF). Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used as evaluation metrics in estimating the performance of models implements and stated that Random forest algorithm is providing 0.07 (RMSE). The outcome of the present research work helps farmers in adopting best management practices and trying to give them the economical and technical support in making easier for them to adopt best management practices.