The new machine learning feature selection method used in fertilizer recommendation

Abstract

Fertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health ,so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer & nutrient recommender in our work from 85% to 98%. Feature selection & raking has been introduced with random forest approach.