Applying Machine Learning Techniques to Extract dosages of Fertilizers for Precision Agriculture

Abstract

Increasing cost of fertilizers and subsidies are proving to be a burden on agriculturists as well as the government. Now it's high time to involve technology so that precise amount of fertilizers could be added in order to obtain maximum yield. Machine learning is an important tool that can be used to predict precise nitrogen, phosphorous and potassium for fertigation. But due to different soil types and different needs of different crops and varieties, it has become difficult to predict the exact amount of fertilizer needed. In this research paper a solution has been drawn to obtain precision in agriculture. Data has been collected from soil reports, soil science institutes and agriculture institutes of India. It has been rearranged into tables and pre-processed for applying machine learning models. Once the models were applied their accuracies have been evaluated using various parameters. Also, the predictions given by our model were compared by already existing recommendations. This work has been done for irrigated wheat growing areas of India and it could be extended to other crops and other areas all over the world.