Abstract

Crop yield prediction is a crucial and challenging task in agriculture due to the complex and non-linear parameters involved. It mainly depends on three fluctuating factors namely soil, weather and environment hence traditional farming methods and statistical methods are no longer feasible to predict the yield precisely therefore Smart farming is gaining more attention with the advent of cutting edge technologies like IoT, Cloud and AI frameworks. Crop yield prediction involves Crop selection, Disease Detection, Weed detection, pest/insect Recognition and Water requirement level to assess the crop quality. This paper intends to provide a literature review on existing techniques to predict the yield of crops using IoT, cloud and Artificial intelligence. The study of 26 selected papers based on exclusion criteria are reviewed chronologically from the year 2017 to 2023 then, outlines the use of different sensors , datasets used in the works considered, simulation platforms ,state-of-the art machine learning and Deep learning algorithms applied as well as the performance metrics used to measure the errors . Finally, the research gaps and challenges faced using these cutting edge technologies are defined.