Agricultural crop yield prediction using optimized artificial neural network approaches

Abstract

World Population is increasing exponentially and the prediction is that it will hit ten billion in the coming years To feed the world agricultural production has to be given priority As long as there is human life on the earth there will be a need for food Thus agriculture is the basic need for human survival Along with every other field agriculture has modernized with both biological and technological developments which includes plant breeding monitoring crops automated maintenance systems use of sensors and the use of agrochemicals By integrating sensors in agriculture huge data is generated leading to the need for algorithms to analyse and figure out a precise solution The low resolution imagery of satellite is used extensively for monitoring crops and forecasting of yield which has a major role to play in the operational systems There are various quantitative and qualitative approaches for low resolution satellite imagery to be used for crop yield prediction In agriculture Big Data can be viewed as a blend of technology and analytics Big Data can handle the huge volume of agricultural data Compared to other traditional methods it collects and compiles novel data and process data effectively Big Data are being capable of providing assistance in agricultural field with patterns attained from the data To handle such satellite images may be very challenging owing to large data amounts Big Data Analysis is effective in dealing with the massive data amounts produced during the prediction of crop yields newline newline