AI ASSISTED FARMING FOR CROP RECOMMENDATION AND FARM YIELD PREDICTION

Abstract

Machine learning is a crucial decision-support tool for forecasting agricultural yields, enabling judgments about which crops to cultivate and what to do when in the growing seasonFor this study,we performed a Systematic Literature Review(SLR) to find and combine the methods and components that are employed in agricultural prediction research. Using inclusion and exclusion criteria from six internet databases, we chose 50 publications out of a total of 567 that met our search criteria for relevancy.We thoroughly examined the chosen publications, applied, and offeredrecommendations for additional studies. Our data show that temperature, rainfall, and soil type are the most often used characteristics in these models, and artificial neural networks are the most frequently used methodology. This observation was based on an examination of 50 publications, and we next looked for studies employing deep learning in additional electronic databases. We gathered the deep learning algorithms from 30 of these publications that we discovered. Convolution Neural Networks(CNN),Long-Short Term Memory(LSTM), and Deep Neural Networks are the three deep learning algorithms that are used in these investigations, according to this additional analysis(DNN).