Agronomics Harvest Endorsement for Fecundity Using Machine Learning and Data Science

Agronomics Harvest Endorsement for Fecundity Using Machine Learning and Data Science

Ms. Rajashree Sutrawe, T. Chrysolite

CSE, Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India

ABSTRACT

Agronomy yield in India is still low in comparison to other countries in the world. In India, the major problems we’re facing in agronomy are unreliable downfall, lack of irrigation installations, soil corrosion, styles of civilization, defective civilization of crops and reduction in net sown area. With these problems, we’re facing difficulty in fecundity. As the population in India is larger and the fruitfulness is lower, we aren’t able to reach the target of supplying food for the people. As the IT world is grows, they want to help the growers by using their Intelligence. We’ve to use our ultramodern technological styles in the agronomy as per our demand to estimate growth by using Machine learning methods. A well-informed decision can directly affect their gains. The use of Random Forest algorithm helps growers understand information about the crop.

Keywords: Agronomics, Endorsement of Harvest, Ultramodern technology, Machine Learning, Random Forest.