Prediction Based Smart Farming

Abstract

The world population is expected to grow by over a third by 2050. Market demand for food will continue to grow. Automated drones and different robots in savvy cultivating applications offer the possibility to screen ranch arrive on a for each plant premise, which thus can diminish the measure of herbicides and pesticides that must be applied. There is a gap between current food productivity growth and needed growth. To boost the yield, farmers switched to extensive use of chemical fertilizers. Excessive fertilizer usage has its negative impact like decreased yield, wastage of fertilizer, damage to soil, and groundwater contamination. Currently, farmers mostly rely on guesswork, estimation, experience when deciding the crop that should grow, and the fertilizer that should be used. In this paper, we have proposed a solution that uses technologies like Machine Learning, Image Processing, and the Internet of things to improvise farm productivity and at the same time, decrease the fertilizer usage. This paper describes the outcomes of a prototype implemented in Rajasthan, India.