Design and Analysis of Prediction Model Using Machine Learning In Agriculture

Abstract

 The reality of worldwide population growth  and climate change demand that agriculture production  can be increased. Traditional study findings which are  difficult to extend to all conceivable fields since these are  dependent on certain soil types, climatic circumstances, and  background management combinations that aren't  appropriate or transferable to all farms. There is no way for  evaluating the efficacy of endless cropping system  interactions (including many management practises) to crop  production across the World. We demonstrate that dynamic  interactions, that cannot be examined in repetitive trials,  which are linked with considerable crop output variability  and therefore the possibility for big yield gains, using  massive databases and artificial intelligence. Our method can  help to speed up agricultural research, discover sustainable  methods, and meet future food demands. This is a paper  attempted that at crop yield prediction using machine  learning techniques with historic crop production data. For  this, data has been collected from data.gov.in and data.world.