An Effective Crop Prediction Using Random Forest Algorithm

Reliable predictions of crop yield are difficult for developing agriculture. Crop production varies by various climatic conditions like dried period, increasing in temperatures remains a huge problem for agriculture workers, governments, and traders to strengthen the need for exactness and analyzing of crop production in a different weather conditions. In this system, a machine-learning method, Random Forest algorithm has an ability to analyze crop growth related to the current climatic conditions and biophysical change. We have collected crop growth datasets from various sources. These datasets are used for both training and testing process. Random Forest classifier was found huge ability to predict crop yield. From different outputs, it shows that Random Forest is an efficient learning algorithm to analyze crop at current climatic condition and has a huge exactness in data investigation.