ABSTRACT
Accurate estimation of crop yield is a challenging field of work. The hardware and software platform to predict the crop yield depends upon various factors like environment, soil fertility, genotype, and various interacting dependents. The task is complex owing to the data that needs to be collected in volumes to understand crop yield through wireless sensor networks and remote sensing. This paper reviews the past 15 years of research work in the development of estimating crop yield using deep learning algorithms. The significance of discussing advancements using deep learning techniques will help in decision making for predicting the crop yield. The hybrid combination of deep learning with remote sensing and wireless sensor networks can provide precision agriculture in the future.
